diff --git a/modules/bioinspired/samples/OpenEXRimages_HDR_Retina_toneMapping.cpp b/modules/bioinspired/samples/OpenEXRimages_HDR_Retina_toneMapping.cpp
index 66cae7719..622b210ae 100644
--- a/modules/bioinspired/samples/OpenEXRimages_HDR_Retina_toneMapping.cpp
+++ b/modules/bioinspired/samples/OpenEXRimages_HDR_Retina_toneMapping.cpp
@@ -16,289 +16,290 @@
 
 static void help(std::string errorMessage)
 {
-    std::cout<<"Program init error : "<<errorMessage<<std::endl;
-    std::cout<<"\nProgram call procedure : ./OpenEXRimages_HDR_Retina_toneMapping [OpenEXR image to process]"<<std::endl;
-    std::cout<<"\t[OpenEXR image to process] : the input HDR image to process, must be an OpenEXR format, see http://www.openexr.com/ to get some samples or create your own using camera bracketing and Photoshop or equivalent software for OpenEXR image synthesis"<<std::endl;
-    std::cout<<"\nExamples:"<<std::endl;
-    std::cout<<"\t-Image processing : ./OpenEXRimages_HDR_Retina_toneMapping memorial.exr"<<std::endl;
+	std::cout<<"Program init error : "<<errorMessage<<std::endl;
+	std::cout<<"\nProgram call procedure : ./OpenEXRimages_HDR_Retina_toneMapping [OpenEXR image to process]"<<std::endl;
+	std::cout<<"\t[OpenEXR image to process] : the input HDR image to process, must be an OpenEXR format, see http://www.openexr.com/ to get some samples or create your own using camera bracketing and Photoshop or equivalent software for OpenEXR image synthesis"<<std::endl;
+	std::cout<<"\nExamples:"<<std::endl;
+	std::cout<<"\t-Image processing : ./OpenEXRimages_HDR_Retina_toneMapping memorial.exr"<<std::endl;
 }
 
 // simple procedure for 1D curve tracing
 static void drawPlot(const cv::Mat curve, const std::string figureTitle, const int lowerLimit, const int upperLimit)
 {
-    //std::cout<<"curve size(h,w) = "<<curve.size().height<<", "<<curve.size().width<<std::endl;
-    cv::Mat displayedCurveImage = cv::Mat::ones(200, curve.size().height, CV_8U);
-
-    cv::Mat windowNormalizedCurve;
-    normalize(curve, windowNormalizedCurve, 0, 200, cv::NORM_MINMAX, CV_32F);
-
-    displayedCurveImage = cv::Scalar::all(255); // set a white background
-    int binW = cvRound((double)displayedCurveImage.cols/curve.size().height);
-
-    for( int i = 0; i < curve.size().height; i++ )
-        rectangle( displayedCurveImage, cv::Point(i*binW, displayedCurveImage.rows),
-                cv::Point((i+1)*binW, displayedCurveImage.rows - cvRound(windowNormalizedCurve.at<float>(i))),
-                cv::Scalar::all(0), -1, 8, 0 );
-    rectangle( displayedCurveImage, cv::Point(0, 0),
-            cv::Point((lowerLimit)*binW, 200),
-            cv::Scalar::all(128), -1, 8, 0 );
-    rectangle( displayedCurveImage, cv::Point(displayedCurveImage.cols, 0),
-            cv::Point((upperLimit)*binW, 200),
-            cv::Scalar::all(128), -1, 8, 0 );
-
-    cv::imshow(figureTitle, displayedCurveImage);
+	//std::cout<<"curve size(h,w) = "<<curve.size().height<<", "<<curve.size().width<<std::endl;
+	cv::Mat displayedCurveImage = cv::Mat::ones(200, curve.size().height, CV_8U);
+
+	cv::Mat windowNormalizedCurve;
+	normalize(curve, windowNormalizedCurve, 0, 200, cv::NORM_MINMAX, CV_32F);
+
+	displayedCurveImage = cv::Scalar::all(255); // set a white background
+	int binW = cvRound((double)displayedCurveImage.cols/curve.size().height);
+
+	for( int i = 0; i < curve.size().height; i++ )
+		rectangle( displayedCurveImage, cv::Point(i*binW, displayedCurveImage.rows),
+				cv::Point((i+1)*binW, displayedCurveImage.rows - cvRound(windowNormalizedCurve.at<float>(i))),
+				cv::Scalar::all(0), -1, 8, 0 );
+	rectangle( displayedCurveImage, cv::Point(0, 0),
+			cv::Point((lowerLimit)*binW, 200),
+			cv::Scalar::all(128), -1, 8, 0 );
+	rectangle( displayedCurveImage, cv::Point(displayedCurveImage.cols, 0),
+			cv::Point((upperLimit)*binW, 200),
+			cv::Scalar::all(128), -1, 8, 0 );
+
+	cv::imshow(figureTitle, displayedCurveImage);
 }
 /*
  * objective : get the gray level map of the input image and rescale it to the range [0-255]
  */
- static void rescaleGrayLevelMat(const cv::Mat &inputMat, cv::Mat &outputMat, const float histogramClippingLimit)
- {
-
-     // adjust output matrix wrt the input size but single channel
-     std::cout<<"Input image rescaling with histogram edges cutting (in order to eliminate bad pixels created during the HDR image creation) :"<<std::endl;
-     //std::cout<<"=> image size (h,w,channels) = "<<inputMat.size().height<<", "<<inputMat.size().width<<", "<<inputMat.channels()<<std::endl;
-     //std::cout<<"=> pixel coding (nbchannel, bytes per channel) = "<<inputMat.elemSize()/inputMat.elemSize1()<<", "<<inputMat.elemSize1()<<std::endl;
-
-     // rescale between 0-255, keeping floating point values
-     cv::normalize(inputMat, outputMat, 0.0, 255.0, cv::NORM_MINMAX);
-
-     // extract a 8bit image that will be used for histogram edge cut
-     cv::Mat intGrayImage;
-     if (inputMat.channels()==1)
-     {
-         outputMat.convertTo(intGrayImage, CV_8U);
-     }else
-     {
-         cv::Mat rgbIntImg;
-         outputMat.convertTo(rgbIntImg, CV_8UC3);
-         cvtColor(rgbIntImg, intGrayImage, cv::COLOR_BGR2GRAY);
-     }
-
-     // get histogram density probability in order to cut values under above edges limits (here 5-95%)... usefull for HDR pixel errors cancellation
-     cv::Mat dst, hist;
-     int histSize = 256;
-     calcHist(&intGrayImage, 1, 0, cv::Mat(), hist, 1, &histSize, 0);
-     cv::Mat normalizedHist;
-     normalize(hist, normalizedHist, 1, 0, cv::NORM_L1, CV_32F); // normalize histogram so that its sum equals 1
-
-     double min_val, max_val;
-     minMaxLoc(normalizedHist, &min_val, &max_val);
-     //std::cout<<"Hist max,min = "<<max_val<<", "<<min_val<<std::endl;
-
-     // compute density probability
-     cv::Mat denseProb=cv::Mat::zeros(normalizedHist.size(), CV_32F);
-     denseProb.at<float>(0)=normalizedHist.at<float>(0);
-     int histLowerLimit=0, histUpperLimit=0;
-     for (int i=1;i<normalizedHist.size().height;++i)
-     {
-         denseProb.at<float>(i)=denseProb.at<float>(i-1)+normalizedHist.at<float>(i);
-         //std::cout<<normalizedHist.at<float>(i)<<", "<<denseProb.at<float>(i)<<std::endl;
-         if ( denseProb.at<float>(i)<histogramClippingLimit)
-             histLowerLimit=i;
-         if ( denseProb.at<float>(i)<1-histogramClippingLimit)
-             histUpperLimit=i;
-     }
-     // deduce min and max admitted gray levels
-     float minInputValue = (float)histLowerLimit/histSize*255;
-     float maxInputValue = (float)histUpperLimit/histSize*255;
-
-     std::cout<<"=> Histogram limits "
-             <<"\n\t"<<histogramClippingLimit*100<<"% index = "<<histLowerLimit<<" => normalizedHist value = "<<denseProb.at<float>(histLowerLimit)<<" => input gray level = "<<minInputValue
-             <<"\n\t"<<(1-histogramClippingLimit)*100<<"% index = "<<histUpperLimit<<" => normalizedHist value = "<<denseProb.at<float>(histUpperLimit)<<" => input gray level = "<<maxInputValue
-             <<std::endl;
-     //drawPlot(denseProb, "input histogram density probability", histLowerLimit, histUpperLimit);
-     drawPlot(normalizedHist, "input histogram", histLowerLimit, histUpperLimit);
-
-     // rescale image range [minInputValue-maxInputValue] to [0-255]
-     outputMat-=minInputValue;
-     outputMat*=255.0/(maxInputValue-minInputValue);
-     // cut original histogram and back project to original image
-     cv::threshold( outputMat, outputMat, 255.0, 255.0, 2 ); //THRESH_TRUNC, clips values above 255
-     cv::threshold( outputMat, outputMat, 0.0, 0.0, 3 ); //THRESH_TOZERO, clips values under 0
-
- }
- // basic callback method for interface management
- cv::Mat inputImage;
- cv::Mat imageInputRescaled;
- int histogramClippingValue;
- static void callBack_rescaleGrayLevelMat(int, void*)
- {
-     std::cout<<"Histogram clipping value changed, current value = "<<histogramClippingValue<<std::endl;
-     rescaleGrayLevelMat(inputImage, imageInputRescaled, (float)(histogramClippingValue/100.0));
-     normalize(imageInputRescaled, imageInputRescaled, 0.0, 255.0, cv::NORM_MINMAX);
- }
-
- cv::Ptr<cv::bioinspired::Retina> retina;
- int retinaHcellsGain;
- int localAdaptation_photoreceptors, localAdaptation_Gcells;
- static void callBack_updateRetinaParams(int, void*)
- {
-     retina->setupOPLandIPLParvoChannel(true, true, (float)(localAdaptation_photoreceptors/200.0), 0.5f, 0.43f, (float)retinaHcellsGain, 1.f, 7.f, (float)(localAdaptation_Gcells/200.0));
- }
-
- int colorSaturationFactor;
- static void callback_saturateColors(int, void*)
- {
-     retina->setColorSaturation(true, (float)colorSaturationFactor);
- }
-
- int main(int argc, char* argv[]) {
-     // welcome message
-     std::cout<<"*********************************************************************************"<<std::endl;
-     std::cout<<"* Retina demonstration for High Dynamic Range compression (tone-mapping) : demonstrates the use of a wrapper class of the Gipsa/Listic Labs retina model."<<std::endl;
-     std::cout<<"* This retina model allows spatio-temporal image processing (applied on still images, video sequences)."<<std::endl;
-     std::cout<<"* This demo focuses demonstration of the dynamic compression capabilities of the model"<<std::endl;
-     std::cout<<"* => the main application is tone mapping of HDR images (i.e. see on a 8bit display a more than 8bits coded (up to 16bits) image with details in high and low luminance ranges"<<std::endl;
-     std::cout<<"* The retina model still have the following properties:"<<std::endl;
-     std::cout<<"* => It applies a spectral whithening (mid-frequency details enhancement)"<<std::endl;
-     std::cout<<"* => high frequency spatio-temporal noise reduction"<<std::endl;
-     std::cout<<"* => low frequency luminance to be reduced (luminance range compression)"<<std::endl;
-     std::cout<<"* => local logarithmic luminance compression allows details to be enhanced in low light conditions\n"<<std::endl;
-     std::cout<<"* for more information, reer to the following papers :"<<std::endl;
-     std::cout<<"* Benoit A., Caplier A., Durette B., Herault, J., \"USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING\", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011"<<std::endl;
-     std::cout<<"* Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891."<<std::endl;
-     std::cout<<"* => reports comments/remarks at benoit.alexandre.vision@gmail.com"<<std::endl;
-     std::cout<<"* => more informations and papers at : http://sites.google.com/site/benoitalexandrevision/"<<std::endl;
-     std::cout<<"*********************************************************************************"<<std::endl;
-     std::cout<<"** WARNING : this sample requires OpenCV to be configured with OpenEXR support **"<<std::endl;
-     std::cout<<"*********************************************************************************"<<std::endl;
-     std::cout<<"*** You can use free tools to generate OpenEXR images from images sets   :    ***"<<std::endl;
-     std::cout<<"*** =>  1. take a set of photos from the same viewpoint using bracketing      ***"<<std::endl;
-     std::cout<<"*** =>  2. generate an OpenEXR image with tools like qtpfsgui.sourceforge.net ***"<<std::endl;
-     std::cout<<"*** =>  3. apply tone mapping with this program                               ***"<<std::endl;
-     std::cout<<"*********************************************************************************"<<std::endl;
-
-     // basic input arguments checking
-     if (argc<2)
-     {
-         help("bad number of parameter");
-         return -1;
-     }
-
-     bool useLogSampling = !strcmp(argv[argc-1], "log"); // check if user wants retina log sampling processing
-     int chosenMethod=0;
-     if (!strcmp(argv[argc-1], "fast"))
-     {
-         chosenMethod=1;
-         std::cout<<"Using fast method (no spectral whithning), adaptation of Meylan&al 2008 method"<<std::endl;
-     }
-
-     std::string inputImageName=argv[1];
-
-     //////////////////////////////////////////////////////////////////////////////
-     // checking input media type (still image, video file, live video acquisition)
-     std::cout<<"RetinaDemo: processing image "<<inputImageName<<std::endl;
-     // image processing case
-     // declare the retina input buffer... that will be fed differently in regard of the input media
-     inputImage = cv::imread(inputImageName, -1); // load image in RGB mode
-     std::cout<<"=> image size (h,w) = "<<inputImage.size().height<<", "<<inputImage.size().width<<std::endl;
-     if (!inputImage.total())
-     {
-        help("could not load image, program end");
-            return -1;
-         }
-     // rescale between 0 and 1
-     normalize(inputImage, inputImage, 0.0, 1.0, cv::NORM_MINMAX);
-     cv::Mat gammaTransformedImage;
-     cv::pow(inputImage, 1./5, gammaTransformedImage); // apply gamma curve: img = img ** (1./5)
-     imshow("EXR image original image, 16bits=>8bits linear rescaling ", inputImage);
-     imshow("EXR image with basic processing : 16bits=>8bits with gamma correction", gammaTransformedImage);
-     if (inputImage.empty())
-     {
-         help("Input image could not be loaded, aborting");
-         return -1;
-     }
-
-     //////////////////////////////////////////////////////////////////////////////
-     // Program start in a try/catch safety context (Retina may throw errors)
-     try
-     {
-         /* create a retina instance with default parameters setup, uncomment the initialisation you wanna test
-          * -> if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
-          */
-         if (useLogSampling)
-         {
-             retina = cv::bioinspired::createRetina(inputImage.size(),true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
-                 }
-         else// -> else allocate "classical" retina :
-             retina = cv::bioinspired::createRetina(inputImage.size());
-
-         // create a fast retina tone mapper (Meyla&al algorithm)
-         std::cout<<"Allocating fast tone mapper..."<<std::endl;
-         //cv::Ptr<cv::RetinaFastToneMapping> fastToneMapper=createRetinaFastToneMapping(inputImage.size());
-         std::cout<<"Fast tone mapper allocated"<<std::endl;
-
-         // save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
-         retina->write("RetinaDefaultParameters.xml");
-
-         // desactivate Magnocellular pathway processing (motion information extraction) since it is not usefull here
-         retina->activateMovingContoursProcessing(false);
-
-         // declare retina output buffers
-         cv::Mat retinaOutput_parvo;
-
-         /////////////////////////////////////////////
-         // prepare displays and interactions
-         histogramClippingValue=0; // default value... updated with interface slider
-         //inputRescaleMat = inputImage;
-         //outputRescaleMat = imageInputRescaled;
-         cv::namedWindow("Processing configuration",1);
-         cv::createTrackbar("histogram edges clipping limit", "Processing configuration",&histogramClippingValue,50,callBack_rescaleGrayLevelMat);
-
-         colorSaturationFactor=3;
-         cv::createTrackbar("Color saturation", "Processing configuration", &colorSaturationFactor,5,callback_saturateColors);
-
-         retinaHcellsGain=40;
-         cv::createTrackbar("Hcells gain", "Processing configuration",&retinaHcellsGain,100,callBack_updateRetinaParams);
-
-         localAdaptation_photoreceptors=197;
-         localAdaptation_Gcells=190;
-         cv::createTrackbar("Ph sensitivity", "Processing configuration", &localAdaptation_photoreceptors,199,callBack_updateRetinaParams);
-         cv::createTrackbar("Gcells sensitivity", "Processing configuration", &localAdaptation_Gcells,199,callBack_updateRetinaParams);
-
-
-         /////////////////////////////////////////////
-         // apply default parameters of user interaction variables
-         rescaleGrayLevelMat(inputImage, imageInputRescaled, (float)histogramClippingValue/100);
-         retina->setColorSaturation(true,(float)colorSaturationFactor);
-         callBack_updateRetinaParams(1,NULL); // first call for default parameters setup
-
-         // processing loop with stop condition
-         bool continueProcessing=true;
-         while(continueProcessing)
-         {
-             // run retina filter
-             if (!chosenMethod)
-             {
-                 retina->run(imageInputRescaled);
-                 // Retrieve and display retina output
-                 retina->getParvo(retinaOutput_parvo);
-                 cv::imshow("Retina input image (with cut edges histogram for basic pixels error avoidance)", imageInputRescaled/255.0);
-                 cv::imshow("Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", retinaOutput_parvo);
-                 cv::imwrite("HDRinput.jpg",imageInputRescaled/255.0);
-                 cv::imwrite("RetinaToneMapping.jpg",retinaOutput_parvo);
-             }
-             else
-             {
-                 // apply the simplified hdr tone mapping method
-                 cv::Mat fastToneMappingOutput;
-                 retina->applyFastToneMapping(imageInputRescaled, fastToneMappingOutput);
-                 cv::imshow("Retina fast tone mapping output : 16bit=>8bit image retina tonemapping", fastToneMappingOutput);
-             }
-             /*cv::Mat fastToneMappingOutput_specificObject;
+static void rescaleGrayLevelMat(const cv::Mat &inputMat, cv::Mat &outputMat, const float histogramClippingLimit)
+{
+
+	// adjust output matrix wrt the input size but single channel
+	std::cout<<"Input image rescaling with histogram edges cutting (in order to eliminate bad pixels created during the HDR image creation) :"<<std::endl;
+	//std::cout<<"=> image size (h,w,channels) = "<<inputMat.size().height<<", "<<inputMat.size().width<<", "<<inputMat.channels()<<std::endl;
+	//std::cout<<"=> pixel coding (nbchannel, bytes per channel) = "<<inputMat.elemSize()/inputMat.elemSize1()<<", "<<inputMat.elemSize1()<<std::endl;
+
+	// rescale between 0-255, keeping floating point values
+	cv::normalize(inputMat, outputMat, 0.0, 255.0, cv::NORM_MINMAX);
+
+	// extract a 8bit image that will be used for histogram edge cut
+	cv::Mat intGrayImage;
+	if (inputMat.channels()==1)
+	{
+		outputMat.convertTo(intGrayImage, CV_8U);
+	}else
+	{
+		cv::Mat rgbIntImg;
+		outputMat.convertTo(rgbIntImg, CV_8UC3);
+		cvtColor(rgbIntImg, intGrayImage, cv::COLOR_BGR2GRAY);
+	}
+
+	// get histogram density probability in order to cut values under above edges limits (here 5-95%)... usefull for HDR pixel errors cancellation
+	cv::Mat dst, hist;
+	int histSize = 256;
+	calcHist(&intGrayImage, 1, 0, cv::Mat(), hist, 1, &histSize, 0);
+	cv::Mat normalizedHist;
+	normalize(hist, normalizedHist, 1, 0, cv::NORM_L1, CV_32F); // normalize histogram so that its sum equals 1
+
+	double min_val, max_val;
+	minMaxLoc(normalizedHist, &min_val, &max_val);
+	//std::cout<<"Hist max,min = "<<max_val<<", "<<min_val<<std::endl;
+
+	// compute density probability
+	cv::Mat denseProb=cv::Mat::zeros(normalizedHist.size(), CV_32F);
+	denseProb.at<float>(0)=normalizedHist.at<float>(0);
+	int histLowerLimit=0, histUpperLimit=0;
+	for (int i=1;i<normalizedHist.size().height;++i)
+	{
+		denseProb.at<float>(i)=denseProb.at<float>(i-1)+normalizedHist.at<float>(i);
+		//std::cout<<normalizedHist.at<float>(i)<<", "<<denseProb.at<float>(i)<<std::endl;
+		if ( denseProb.at<float>(i)<histogramClippingLimit)
+			histLowerLimit=i;
+		if ( denseProb.at<float>(i)<1-histogramClippingLimit)
+			histUpperLimit=i;
+	}
+	// deduce min and max admitted gray levels
+	float minInputValue = (float)histLowerLimit/histSize*255;
+	float maxInputValue = (float)histUpperLimit/histSize*255;
+
+	std::cout<<"=> Histogram limits "
+			<<"\n\t"<<histogramClippingLimit*100<<"% index = "<<histLowerLimit<<" => normalizedHist value = "<<denseProb.at<float>(histLowerLimit)<<" => input gray level = "<<minInputValue
+			<<"\n\t"<<(1-histogramClippingLimit)*100<<"% index = "<<histUpperLimit<<" => normalizedHist value = "<<denseProb.at<float>(histUpperLimit)<<" => input gray level = "<<maxInputValue
+			<<std::endl;
+	//drawPlot(denseProb, "input histogram density probability", histLowerLimit, histUpperLimit);
+	drawPlot(normalizedHist, "input histogram", histLowerLimit, histUpperLimit);
+
+	// rescale image range [minInputValue-maxInputValue] to [0-255]
+	outputMat-=minInputValue;
+	outputMat*=255.0/(maxInputValue-minInputValue);
+	// cut original histogram and back project to original image
+	cv::threshold( outputMat, outputMat, 255.0, 255.0, 2 ); //THRESH_TRUNC, clips values above 255
+	cv::threshold( outputMat, outputMat, 0.0, 0.0, 3 ); //THRESH_TOZERO, clips values under 0
+
+}
+// basic callback method for interface management
+cv::Mat inputImage;
+cv::Mat imageInputRescaled;
+int histogramClippingValue;
+static void callBack_rescaleGrayLevelMat(int, void*)
+{
+	std::cout<<"Histogram clipping value changed, current value = "<<histogramClippingValue<<std::endl;
+	rescaleGrayLevelMat(inputImage, imageInputRescaled, (float)(histogramClippingValue/100.0));
+	normalize(imageInputRescaled, imageInputRescaled, 0.0, 255.0, cv::NORM_MINMAX);
+}
+
+cv::Ptr<cv::bioinspired::Retina> retina;
+int retinaHcellsGain;
+int localAdaptation_photoreceptors, localAdaptation_Gcells;
+static void callBack_updateRetinaParams(int, void*)
+{
+	retina->setupOPLandIPLParvoChannel(true, true, (float)(localAdaptation_photoreceptors/200.0), 0.5f, 0.43f, (float)retinaHcellsGain, 1.f, 7.f, (float)(localAdaptation_Gcells/200.0));
+}
+
+int colorSaturationFactor;
+static void callback_saturateColors(int, void*)
+{
+	retina->setColorSaturation(true, (float)colorSaturationFactor);
+}
+
+int main(int argc, char* argv[])
+{
+	// welcome message
+	std::cout<<"*********************************************************************************"<<std::endl;
+	std::cout<<"* Retina demonstration for High Dynamic Range compression (tone-mapping) : demonstrates the use of a wrapper class of the Gipsa/Listic Labs retina model."<<std::endl;
+	std::cout<<"* This retina model allows spatio-temporal image processing (applied on still images, video sequences)."<<std::endl;
+	std::cout<<"* This demo focuses demonstration of the dynamic compression capabilities of the model"<<std::endl;
+	std::cout<<"* => the main application is tone mapping of HDR images (i.e. see on a 8bit display a more than 8bits coded (up to 16bits) image with details in high and low luminance ranges"<<std::endl;
+	std::cout<<"* The retina model still have the following properties:"<<std::endl;
+	std::cout<<"* => It applies a spectral whithening (mid-frequency details enhancement)"<<std::endl;
+	std::cout<<"* => high frequency spatio-temporal noise reduction"<<std::endl;
+	std::cout<<"* => low frequency luminance to be reduced (luminance range compression)"<<std::endl;
+	std::cout<<"* => local logarithmic luminance compression allows details to be enhanced in low light conditions\n"<<std::endl;
+	std::cout<<"* for more information, reer to the following papers :"<<std::endl;
+	std::cout<<"* Benoit A., Caplier A., Durette B., Herault, J., \"USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING\", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011"<<std::endl;
+	std::cout<<"* Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891."<<std::endl;
+	std::cout<<"* => reports comments/remarks at benoit.alexandre.vision@gmail.com"<<std::endl;
+	std::cout<<"* => more informations and papers at : http://sites.google.com/site/benoitalexandrevision/"<<std::endl;
+	std::cout<<"*********************************************************************************"<<std::endl;
+	std::cout<<"** WARNING : this sample requires OpenCV to be configured with OpenEXR support **"<<std::endl;
+	std::cout<<"*********************************************************************************"<<std::endl;
+	std::cout<<"*** You can use free tools to generate OpenEXR images from images sets   :    ***"<<std::endl;
+	std::cout<<"*** =>  1. take a set of photos from the same viewpoint using bracketing      ***"<<std::endl;
+	std::cout<<"*** =>  2. generate an OpenEXR image with tools like qtpfsgui.sourceforge.net ***"<<std::endl;
+	std::cout<<"*** =>  3. apply tone mapping with this program                               ***"<<std::endl;
+	std::cout<<"*********************************************************************************"<<std::endl;
+
+	// basic input arguments checking
+	if (argc<2)
+	{
+		help("bad number of parameter");
+		return -1;
+	}
+
+	bool useLogSampling = !strcmp(argv[argc-1], "log"); // check if user wants retina log sampling processing
+	int chosenMethod=0;
+	if (!strcmp(argv[argc-1], "fast"))
+	{
+		chosenMethod=1;
+		std::cout<<"Using fast method (no spectral whithning), adaptation of Meylan&al 2008 method"<<std::endl;
+	}
+
+	std::string inputImageName=argv[1];
+
+	//////////////////////////////////////////////////////////////////////////////
+	// checking input media type (still image, video file, live video acquisition)
+	std::cout<<"RetinaDemo: processing image "<<inputImageName<<std::endl;
+	// image processing case
+	// declare the retina input buffer... that will be fed differently in regard of the input media
+	inputImage = cv::imread(inputImageName, -1); // load image in RGB mode
+	std::cout<<"=> image size (h,w) = "<<inputImage.size().height<<", "<<inputImage.size().width<<std::endl;
+	if (!inputImage.total())
+	{
+		help("could not load image, program end");
+		return -1;
+	}
+	// rescale between 0 and 1
+	normalize(inputImage, inputImage, 0.0, 1.0, cv::NORM_MINMAX);
+	cv::Mat gammaTransformedImage;
+	cv::pow(inputImage, 1./5, gammaTransformedImage); // apply gamma curve: img = img ** (1./5)
+	imshow("EXR image original image, 16bits=>8bits linear rescaling ", inputImage);
+	imshow("EXR image with basic processing : 16bits=>8bits with gamma correction", gammaTransformedImage);
+	if (inputImage.empty())
+	{
+		help("Input image could not be loaded, aborting");
+		return -1;
+	}
+
+	//////////////////////////////////////////////////////////////////////////////
+	// Program start in a try/catch safety context (Retina may throw errors)
+	try
+	{
+		/* create a retina instance with default parameters setup, uncomment the initialisation you wanna test
+		 * -> if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
+		 */
+		if (useLogSampling)
+		{
+			retina = cv::bioinspired::createRetina(inputImage.size(),true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
+		}
+		else// -> else allocate "classical" retina :
+			retina = cv::bioinspired::createRetina(inputImage.size());
+
+		// create a fast retina tone mapper (Meyla&al algorithm)
+		std::cout<<"Allocating fast tone mapper..."<<std::endl;
+		//cv::Ptr<cv::RetinaFastToneMapping> fastToneMapper=createRetinaFastToneMapping(inputImage.size());
+		std::cout<<"Fast tone mapper allocated"<<std::endl;
+
+		// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
+		retina->write("RetinaDefaultParameters.xml");
+
+		// desactivate Magnocellular pathway processing (motion information extraction) since it is not usefull here
+		retina->activateMovingContoursProcessing(false);
+
+		// declare retina output buffers
+		cv::Mat retinaOutput_parvo;
+
+		/////////////////////////////////////////////
+		// prepare displays and interactions
+		histogramClippingValue=0; // default value... updated with interface slider
+		//inputRescaleMat = inputImage;
+		//outputRescaleMat = imageInputRescaled;
+		cv::namedWindow("Processing configuration",1);
+		cv::createTrackbar("histogram edges clipping limit", "Processing configuration",&histogramClippingValue,50,callBack_rescaleGrayLevelMat);
+
+		colorSaturationFactor=3;
+		cv::createTrackbar("Color saturation", "Processing configuration", &colorSaturationFactor,5,callback_saturateColors);
+
+		retinaHcellsGain=40;
+		cv::createTrackbar("Hcells gain", "Processing configuration",&retinaHcellsGain,100,callBack_updateRetinaParams);
+
+		localAdaptation_photoreceptors=197;
+		localAdaptation_Gcells=190;
+		cv::createTrackbar("Ph sensitivity", "Processing configuration", &localAdaptation_photoreceptors,199,callBack_updateRetinaParams);
+		cv::createTrackbar("Gcells sensitivity", "Processing configuration", &localAdaptation_Gcells,199,callBack_updateRetinaParams);
+
+
+		/////////////////////////////////////////////
+		// apply default parameters of user interaction variables
+		rescaleGrayLevelMat(inputImage, imageInputRescaled, (float)histogramClippingValue/100);
+		retina->setColorSaturation(true,(float)colorSaturationFactor);
+		callBack_updateRetinaParams(1,NULL); // first call for default parameters setup
+
+		// processing loop with stop condition
+		bool continueProcessing=true;
+		while(continueProcessing)
+		{
+			// run retina filter
+			if (!chosenMethod)
+			{
+				retina->run(imageInputRescaled);
+				// Retrieve and display retina output
+				retina->getParvo(retinaOutput_parvo);
+				cv::imshow("Retina input image (with cut edges histogram for basic pixels error avoidance)", imageInputRescaled/255.0);
+				cv::imshow("Retina Parvocellular pathway output : 16bit=>8bit image retina tonemapping", retinaOutput_parvo);
+				cv::imwrite("HDRinput.jpg",imageInputRescaled/255.0);
+				cv::imwrite("RetinaToneMapping.jpg",retinaOutput_parvo);
+			}
+			else
+			{
+				// apply the simplified hdr tone mapping method
+				cv::Mat fastToneMappingOutput;
+				retina->applyFastToneMapping(imageInputRescaled, fastToneMappingOutput);
+				cv::imshow("Retina fast tone mapping output : 16bit=>8bit image retina tonemapping", fastToneMappingOutput);
+			}
+			/*cv::Mat fastToneMappingOutput_specificObject;
              fastToneMapper->setup(3.f, 1.5f, 1.f);
              fastToneMapper->applyFastToneMapping(imageInputRescaled, fastToneMappingOutput_specificObject);
              cv::imshow("### Retina fast tone mapping output : 16bit=>8bit image retina tonemapping", fastToneMappingOutput_specificObject);
-*/
-             cv::waitKey(10);
-         }
-     }catch(cv::Exception e)
-     {
-         std::cerr<<"Error using Retina : "<<e.what()<<std::endl;
-     }
-
-     // Program end message
-     std::cout<<"Retina demo end"<<std::endl;
-
-     return 0;
- }
+			 */
+			cv::waitKey(10);
+		}
+	}catch(cv::Exception e)
+	{
+		std::cerr<<"Error using Retina : "<<e.what()<<std::endl;
+	}
+
+	// Program end message
+	std::cout<<"Retina demo end"<<std::endl;
+
+	return 0;
+}
diff --git a/modules/bioinspired/samples/retinaDemo.cpp b/modules/bioinspired/samples/retinaDemo.cpp
index 790e75383..24b04f746 100644
--- a/modules/bioinspired/samples/retinaDemo.cpp
+++ b/modules/bioinspired/samples/retinaDemo.cpp
@@ -14,145 +14,146 @@
 
 static void help(std::string errorMessage)
 {
-    std::cout<<"Program init error : "<<errorMessage<<std::endl;
-    std::cout<<"\nProgram call procedure : retinaDemo [processing mode] [Optional : media target] [Optional LAST parameter: \"log\" to activate retina log sampling]"<<std::endl;
-    std::cout<<"\t[processing mode] :"<<std::endl;
-    std::cout<<"\t -image : for still image processing"<<std::endl;
-    std::cout<<"\t -video : for video stream processing"<<std::endl;
-    std::cout<<"\t[Optional : media target] :"<<std::endl;
-    std::cout<<"\t if processing an image or video file, then, specify the path and filename of the target to process"<<std::endl;
-    std::cout<<"\t leave empty if processing video stream coming from a connected video device"<<std::endl;
-    std::cout<<"\t[Optional : activate retina log sampling] : an optional last parameter can be specified for retina spatial log sampling"<<std::endl;
-    std::cout<<"\t set \"log\" without quotes to activate this sampling, output frame size will be divided by 4"<<std::endl;
-    std::cout<<"\nExamples:"<<std::endl;
-    std::cout<<"\t-Image processing : ./retinaDemo -image lena.jpg"<<std::endl;
-    std::cout<<"\t-Image processing with log sampling : ./retinaDemo -image lena.jpg log"<<std::endl;
-    std::cout<<"\t-Video processing : ./retinaDemo -video myMovie.mp4"<<std::endl;
-    std::cout<<"\t-Live video processing : ./retinaDemo -video"<<std::endl;
-    std::cout<<"\nPlease start again with new parameters"<<std::endl;
+	std::cout<<"Program init error : "<<errorMessage<<std::endl;
+	std::cout<<"\nProgram call procedure : retinaDemo [processing mode] [Optional : media target] [Optional LAST parameter: \"log\" to activate retina log sampling]"<<std::endl;
+	std::cout<<"\t[processing mode] :"<<std::endl;
+	std::cout<<"\t -image : for still image processing"<<std::endl;
+	std::cout<<"\t -video : for video stream processing"<<std::endl;
+	std::cout<<"\t[Optional : media target] :"<<std::endl;
+	std::cout<<"\t if processing an image or video file, then, specify the path and filename of the target to process"<<std::endl;
+	std::cout<<"\t leave empty if processing video stream coming from a connected video device"<<std::endl;
+	std::cout<<"\t[Optional : activate retina log sampling] : an optional last parameter can be specified for retina spatial log sampling"<<std::endl;
+	std::cout<<"\t set \"log\" without quotes to activate this sampling, output frame size will be divided by 4"<<std::endl;
+	std::cout<<"\nExamples:"<<std::endl;
+	std::cout<<"\t-Image processing : ./retinaDemo -image lena.jpg"<<std::endl;
+	std::cout<<"\t-Image processing with log sampling : ./retinaDemo -image lena.jpg log"<<std::endl;
+	std::cout<<"\t-Video processing : ./retinaDemo -video myMovie.mp4"<<std::endl;
+	std::cout<<"\t-Live video processing : ./retinaDemo -video"<<std::endl;
+	std::cout<<"\nPlease start again with new parameters"<<std::endl;
 }
 
-int main(int argc, char* argv[]) {
-    // welcome message
-    std::cout<<"****************************************************"<<std::endl;
-    std::cout<<"* Retina demonstration : demonstrates the use of is a wrapper class of the Gipsa/Listic Labs retina model."<<std::endl;
-    std::cout<<"* This retina model allows spatio-temporal image processing (applied on still images, video sequences)."<<std::endl;
-    std::cout<<"* As a summary, these are the retina model properties:"<<std::endl;
-    std::cout<<"* => It applies a spectral whithening (mid-frequency details enhancement)"<<std::endl;
-    std::cout<<"* => high frequency spatio-temporal noise reduction"<<std::endl;
-    std::cout<<"* => low frequency luminance to be reduced (luminance range compression)"<<std::endl;
-    std::cout<<"* => local logarithmic luminance compression allows details to be enhanced in low light conditions\n"<<std::endl;
-    std::cout<<"* for more information, reer to the following papers :"<<std::endl;
-    std::cout<<"* Benoit A., Caplier A., Durette B., Herault, J., \"USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING\", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011"<<std::endl;
-    std::cout<<"* Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891."<<std::endl;
-    std::cout<<"* => reports comments/remarks at benoit.alexandre.vision@gmail.com"<<std::endl;
-    std::cout<<"* => more informations and papers at : http://sites.google.com/site/benoitalexandrevision/"<<std::endl;
-    std::cout<<"****************************************************"<<std::endl;
-    std::cout<<" NOTE : this program generates the default retina parameters file 'RetinaDefaultParameters.xml'"<<std::endl;
-    std::cout<<" => you can use this to fine tune parameters and load them if you save to file 'RetinaSpecificParameters.xml'"<<std::endl;
-
-    // basic input arguments checking
-    if (argc<2)
-    {
-        help("bad number of parameter");
-        return -1;
-    }
-
-    bool useLogSampling = !strcmp(argv[argc-1], "log"); // check if user wants retina log sampling processing
-
-    std::string inputMediaType=argv[1];
-
-    // declare the retina input buffer... that will be fed differently in regard of the input media
-    cv::Mat inputFrame;
-    cv::VideoCapture videoCapture; // in case a video media is used, its manager is declared here
-
-    //////////////////////////////////////////////////////////////////////////////
-    // checking input media type (still image, video file, live video acquisition)
-    if (!strcmp(inputMediaType.c_str(), "-image") && argc >= 3)
-    {
-        std::cout<<"RetinaDemo: processing image "<<argv[2]<<std::endl;
-        // image processing case
-        inputFrame = cv::imread(std::string(argv[2]), 1); // load image in RGB mode
-    }else
-        if (!strcmp(inputMediaType.c_str(), "-video"))
-        {
-            if (argc == 2 || (argc == 3 && useLogSampling)) // attempt to grab images from a video capture device
-            {
-                videoCapture.open(0);
-            }else// attempt to grab images from a video filestream
-            {
-                std::cout<<"RetinaDemo: processing video stream "<<argv[2]<<std::endl;
-                videoCapture.open(argv[2]);
-            }
-
-            // grab a first frame to check if everything is ok
-            videoCapture>>inputFrame;
-        }else
-        {
-            // bad command parameter
-            help("bad command parameter");
-            return -1;
-        }
-
-    if (inputFrame.empty())
-    {
-        help("Input media could not be loaded, aborting");
-        return -1;
-    }
-
-
-    //////////////////////////////////////////////////////////////////////////////
-    // Program start in a try/catch safety context (Retina may throw errors)
-    try
-    {
-        // create a retina instance with default parameters setup, uncomment the initialisation you wanna test
-        cv::Ptr<cv::bioinspired::Retina> myRetina;
-
-        // if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
-        if (useLogSampling)
-                {
-                        myRetina = cv::bioinspired::createRetina(inputFrame.size(), true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
-                }
-        else// -> else allocate "classical" retina :
-            myRetina = cv::bioinspired::createRetina(inputFrame.size());
-
-        // save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
-        myRetina->write("RetinaDefaultParameters.xml");
-
-        // load parameters if file exists
-        myRetina->setup("RetinaSpecificParameters.xml");
-        myRetina->clearBuffers();
-
-        // declare retina output buffers
-        cv::Mat retinaOutput_parvo;
-        cv::Mat retinaOutput_magno;
-
-        // processing loop with stop condition
-        bool continueProcessing=true; // FIXME : not yet managed during process...
-        while(continueProcessing)
-        {
-            // if using video stream, then, grabbing a new frame, else, input remains the same
-            if (videoCapture.isOpened())
-                videoCapture>>inputFrame;
-
-            // run retina filter
-            myRetina->run(inputFrame);
-            // Retrieve and display retina output
-            myRetina->getParvo(retinaOutput_parvo);
-            myRetina->getMagno(retinaOutput_magno);
-            cv::imshow("retina input", inputFrame);
-            cv::imshow("Retina Parvo", retinaOutput_parvo);
-            cv::imshow("Retina Magno", retinaOutput_magno);
-
-            cv::waitKey(5);
-        }
-    }catch(cv::Exception e)
-    {
-        std::cerr<<"Error using Retina : "<<e.what()<<std::endl;
-    }
-
-    // Program end message
-    std::cout<<"Retina demo end"<<std::endl;
-
-    return 0;
+int main(int argc, char* argv[])
+{
+	// welcome message
+	std::cout<<"****************************************************"<<std::endl;
+	std::cout<<"* Retina demonstration : demonstrates the use of is a wrapper class of the Gipsa/Listic Labs retina model."<<std::endl;
+	std::cout<<"* This retina model allows spatio-temporal image processing (applied on still images, video sequences)."<<std::endl;
+	std::cout<<"* As a summary, these are the retina model properties:"<<std::endl;
+	std::cout<<"* => It applies a spectral whithening (mid-frequency details enhancement)"<<std::endl;
+	std::cout<<"* => high frequency spatio-temporal noise reduction"<<std::endl;
+	std::cout<<"* => low frequency luminance to be reduced (luminance range compression)"<<std::endl;
+	std::cout<<"* => local logarithmic luminance compression allows details to be enhanced in low light conditions\n"<<std::endl;
+	std::cout<<"* for more information, reer to the following papers :"<<std::endl;
+	std::cout<<"* Benoit A., Caplier A., Durette B., Herault, J., \"USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING\", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011"<<std::endl;
+	std::cout<<"* Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891."<<std::endl;
+	std::cout<<"* => reports comments/remarks at benoit.alexandre.vision@gmail.com"<<std::endl;
+	std::cout<<"* => more informations and papers at : http://sites.google.com/site/benoitalexandrevision/"<<std::endl;
+	std::cout<<"****************************************************"<<std::endl;
+	std::cout<<" NOTE : this program generates the default retina parameters file 'RetinaDefaultParameters.xml'"<<std::endl;
+	std::cout<<" => you can use this to fine tune parameters and load them if you save to file 'RetinaSpecificParameters.xml'"<<std::endl;
+
+	// basic input arguments checking
+	if (argc<2)
+	{
+		help("bad number of parameter");
+		return -1;
+	}
+
+	bool useLogSampling = !strcmp(argv[argc-1], "log"); // check if user wants retina log sampling processing
+
+	std::string inputMediaType=argv[1];
+
+	// declare the retina input buffer... that will be fed differently in regard of the input media
+	cv::Mat inputFrame;
+	cv::VideoCapture videoCapture; // in case a video media is used, its manager is declared here
+
+	//////////////////////////////////////////////////////////////////////////////
+	// checking input media type (still image, video file, live video acquisition)
+	if (!strcmp(inputMediaType.c_str(), "-image") && argc >= 3)
+	{
+		std::cout<<"RetinaDemo: processing image "<<argv[2]<<std::endl;
+		// image processing case
+		inputFrame = cv::imread(std::string(argv[2]), 1); // load image in RGB mode
+	}else
+		if (!strcmp(inputMediaType.c_str(), "-video"))
+		{
+			if (argc == 2 || (argc == 3 && useLogSampling)) // attempt to grab images from a video capture device
+			{
+				videoCapture.open(0);
+			}else// attempt to grab images from a video filestream
+			{
+				std::cout<<"RetinaDemo: processing video stream "<<argv[2]<<std::endl;
+				videoCapture.open(argv[2]);
+			}
+
+			// grab a first frame to check if everything is ok
+			videoCapture>>inputFrame;
+		}else
+		{
+			// bad command parameter
+			help("bad command parameter");
+			return -1;
+		}
+
+	if (inputFrame.empty())
+	{
+		help("Input media could not be loaded, aborting");
+		return -1;
+	}
+
+
+	//////////////////////////////////////////////////////////////////////////////
+	// Program start in a try/catch safety context (Retina may throw errors)
+	try
+	{
+		// create a retina instance with default parameters setup, uncomment the initialisation you wanna test
+		cv::Ptr<cv::bioinspired::Retina> myRetina;
+
+		// if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
+		if (useLogSampling)
+		{
+			myRetina = cv::bioinspired::createRetina(inputFrame.size(), true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
+		}
+		else// -> else allocate "classical" retina :
+			myRetina = cv::bioinspired::createRetina(inputFrame.size());
+
+		// save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
+		myRetina->write("RetinaDefaultParameters.xml");
+
+		// load parameters if file exists
+		myRetina->setup("RetinaSpecificParameters.xml");
+		myRetina->clearBuffers();
+
+		// declare retina output buffers
+		cv::Mat retinaOutput_parvo;
+		cv::Mat retinaOutput_magno;
+
+		// processing loop with stop condition
+		bool continueProcessing=true; // FIXME : not yet managed during process...
+		while(continueProcessing)
+		{
+			// if using video stream, then, grabbing a new frame, else, input remains the same
+			if (videoCapture.isOpened())
+				videoCapture>>inputFrame;
+
+			// run retina filter
+			myRetina->run(inputFrame);
+			// Retrieve and display retina output
+			myRetina->getParvo(retinaOutput_parvo);
+			myRetina->getMagno(retinaOutput_magno);
+			cv::imshow("retina input", inputFrame);
+			cv::imshow("Retina Parvo", retinaOutput_parvo);
+			cv::imshow("Retina Magno", retinaOutput_magno);
+
+			cv::waitKey(5);
+		}
+	}catch(cv::Exception e)
+	{
+		std::cerr<<"Error using Retina : "<<e.what()<<std::endl;
+	}
+
+	// Program end message
+	std::cout<<"Retina demo end"<<std::endl;
+
+	return 0;
 }