Class which provides the main controls to the Gipsa/Listic labs human retina model. This is a non separable spatio-temporal filter modelling the two main retina information channels :
From a general point of view, this filter whitens the image spectrum and corrects luminance thanks to local adaptation. An other important property is its hability to filter out spatio-temporal noise while enhancing details.
This model originates from Jeanny Herault work [Herault2010]_. It has been involved in Alexandre Benoit phd and his current research [Benoit2010]_ (he currently maintains this module within OpenCV). It includes the work of other Jeanny's phd student such as [Chaix2007]_ and the log polar transformations of Barthelemy Durette described in Jeanny's book.
* For ease of use in computer vision applications, the two retina channels are applied homogeneously on all the input images. This does not follow the real retina topology but this can still be done using the log sampling capabilities proposed within the class.
* Extend the retina description and code use in the tutorial/contrib section for complementary explanations.
As a preliminary presentation, let's start with a visual example. We propose to apply the filter on a low quality color jpeg image with backlight problems. Here is the considered input... *"Well, my eyes were able to see more that this strange black shadow..."*
Below, the retina foveal model applied on the entire image with default parameters. Here contours are enforced, halo effects are voluntary visible with this configuration. See parameters discussion below and increase horizontalCellsGain near 1 to remove them.
:alt:the retina foveal model applied on the entire image with default parameters. Here contours are enforced, luminance is corrected and halo effects are voluntary visible with this configuration, increase horizontalCellsGain near 1 to remove them.
Below, a second retina foveal model output applied on the entire image with a parameters setup focused on naturalness perception. *"Hey, i now recognize my cat, looking at the mountains at the end of the day !"*. Here contours are enforced, luminance is corrected but halos are avoided with this configuration. The backlight effect is corrected and highlight details are still preserved. Then, even on a low quality jpeg image, if some luminance information remains, the retina is able to reconstruct a proper visual signal. Such configuration is also usefull for High Dynamic Range (*HDR*) images compression to 8bit images as discussed in [benoit2010]_ and in the demonstration codes discussed below.
:alt:the retina foveal model applied on the entire image with 'naturalness' parameters. Here contours are enforced but are avoided with this configuration, horizontalCellsGain is 0.3 and photoreceptorsLocalAdaptationSensitivity=ganglioncellsSensitivity=0.89.
As observed in this preliminary demo, the retina can be settled up with various parameters, by default, as shown on the figure above, the retina strongly reduces mean luminance energy and enforces all details of the visual scene. Luminance energy and halo effects can be modulated (exagerated to cancelled as shown on the two examples). In order to use your own parameters, you can use at least one time the *write(String fs)* method which will write a proper XML file with all default parameters. Then, tweak it on your own and reload them at any time using method *setup(String fs)*. These methods update a *Retina::RetinaParameters* member structure that is described hereafter. XML parameters file samples are shown at the end of the page.
Class which allows the `Gipsa <http://www.gipsa-lab.inpg.fr>`_ (preliminary work) / `Listic <http://www.listic.univ-savoie.fr>`_ (code maintainer and user) labs retina model to be used. This class allows human retina spatio-temporal image processing to be applied on still images, images sequences and video sequences. Briefly, here are the main human retina model properties:
* performing texture analysis with enhanced signal to noise ratio and enhanced details robust against input images luminance ranges (check out the parvocellular retina channel output, by using the provided **getParvo** methods)
* performing motion analysis also taking benefit of the previously cited properties (check out the magnocellular retina channel output, by using the provided **getMagno** methods)
..[Benoit2010] 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>
..[Chaix2007] B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007
* take a look at *imagelogpolprojection.hpp* to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions. More informations in the above cited Jeanny Heraults's book.
***samples/cpp/retinademo.cpp** shows how to use the retina module for details enhancement (Parvo channel output) and transient maps observation (Magno channel output). You can play with images, video sequences and webcam video.
**Note :** This demo generates the file *RetinaDefaultParameters.xml* which contains the default parameters of the retina. Then, rename this as *RetinaSpecificParameters.xml*, adjust the parameters the way you want and reload the program to check the effect.
***samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping.cpp** shows how to use the retina to perform High Dynamic Range (HDR) luminance compression
Then, take a HDR image using bracketing with your camera and generate an OpenEXR image and then process it using the demo.
Typical use, supposing that you have the OpenEXR image *memorial.exr* (present in the samples/cpp/ folder)
:param useRetinaLogSampling:activate retina log sampling, if true, the 2 following parameters can be used
:param reductionFactor:only usefull if param useRetinaLogSampling=true, specifies the reduction factor of the output frame (as the center (fovea) is high resolution and corners can be underscaled, then a reduction of the output is allowed without precision leak
:param samplingStrenght:only usefull if param useRetinaLogSampling=true, specifies the strenght of the log scale that is applied
:param activate:true if Parvocellular (contours information extraction) output should be activated, false if not... if activated, the Parvocellular output can be retrieved using the **getParvo** methods
:param activate:true if Magnocellular output should be activated, false if not... if activated, the Magnocellular output can be retrieved using the **getMagno** methods
Clears all retina buffers (equivalent to opening the eyes after a long period of eye close ;o) whatchout the temporal transition occuring just after this method call.
Accessor of the details channel of the retina (models foveal vision). Warning, getParvoRAW methods return buffers that are not rescaled within range [0;255] while the non RAW method allows a normalized matrix to be retrieved.
* RAW methods actually return a 1D matrix (encoding is R1, R2, ... Rn, G1, G2, ..., Gn, B1, B2, ...Bn), this output is the original retina filter model output, without any quantification or rescaling.
Accessor of the motion channel of the retina (models peripheral vision). Warning, getMagnoRAW methods return buffers that are not rescaled within range [0;255] while the non RAW method allows a normalized matrix to be retrieved.
* RAW methods actually return a 1D matrix (encoding is M1, M2,... Mn), this output is the original retina filter model output, without any quantification or rescaling.
Method which allows retina to be applied on an input image, after run, encapsulated retina module is ready to deliver its outputs using dedicated acccessors, see getParvo and getMagno methods
Activate color saturation as the final step of the color demultiplexing process -> this saturation is a sigmoide function applied to each channel of the demultiplexed image.
Try to open an XML retina parameters file to adjust current retina instance setup => if the xml file does not exist, then default setup is applied => warning, Exceptions are thrown if read XML file is not valid
:param newParameters:a parameters structures updated with the new target configuration. You can retreive the current parameers structure using method *Retina::RetinaParameters Retina::getParameters()* and update it before running method *setup*.
Set parameters values for the Inner Plexiform Layer (IPL) magnocellular channel this channel processes signals output from OPL processing stage in peripheral vision, it allows motion information enhancement. It is decorrelated from the details channel. See reference papers for more details.
:param normaliseOutput:specifies if (true) output is rescaled between 0 and 255 of not (false)
:param parasolCells_beta:the low pass filter gain used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), typical value is 0
:param parasolCells_tau:the low pass filter time constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical value is 0 (immediate response)
:param parasolCells_k:the low pass filter spatial constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical value is 5
:param amacrinCellsTemporalCutFrequency:the time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, typical value is 1.2
:param V0CompressionParameter:the compression strengh of the ganglion cells local adaptation output, set a value between 0.6 and 1 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 0.95
:param localAdaptintegration_tau:specifies the temporal constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
:param localAdaptintegration_k:specifies the spatial constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
Setup the OPL and IPL parvo channels (see biologocal model) OPL is referred as Outer Plexiform Layer of the retina, it allows the spatio-temporal filtering which withens the spectrum and reduces spatio-temporal noise while attenuating global luminance (low frequency energy) IPL parvo is the OPL next processing stage, it refers to a part of the Inner Plexiform layer of the retina, it allows high contours sensitivity in foveal vision. See reference papers for more informations.
:param colorMode:specifies if (true) color is processed of not (false) to then processing gray level image
:param normaliseOutput:specifies if (true) output is rescaled between 0 and 255 of not (false)
:param photoreceptorsLocalAdaptationSensitivity:the photoreceptors sensitivity renage is 0-1 (more log compression effect when value increases)
:param photoreceptorsTemporalConstant:the time constant of the first order low pass filter of the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 1 frame
:param photoreceptorsSpatialConstant:the spatial constant of the first order low pass filter of the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 1 pixel
:param horizontalCellsGain:gain of the horizontal cells network, if 0, then the mean value of the output is zero, if the parameter is near 1, then, the luminance is not filtered and is still reachable at the output, typicall value is 0
:param HcellsTemporalConstant:the time constant of the first order low pass filter of the horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is frames, typical value is 1 frame, as the photoreceptors
:param HcellsSpatialConstant:the spatial constant of the first order low pass filter of the horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, typical value is 5 pixel, this value is also used for local contrast computing when computing the local contrast adaptation at the ganglion cells level (Inner Plexiform Layer parvocellular channel model)
:param ganglionCellsSensitivity:the compression strengh of the ganglion cells local adaptation output, set a value between 0.6 and 1 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 0.7
This structure merges all the parameters that can be adjusted threw the **Retina::setup()**, **Retina::setupOPLandIPLParvoChannel** and **Retina::setupIPLMagnoChannel** setup methods
normaliseOutput(true), // specifies if (true) output is rescaled between 0 and 255 of not (false)
photoreceptorsLocalAdaptationSensitivity(0.7f), // the photoreceptors sensitivity renage is 0-1 (more log compression effect when value increases)
photoreceptorsTemporalConstant(0.5f),// the time constant of the first order low pass filter of the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 1 frame
photoreceptorsSpatialConstant(0.53f),// the spatial constant of the first order low pass filter of the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 1 pixel
horizontalCellsGain(0.0f),//gain of the horizontal cells network, if 0, then the mean value of the output is zero, if the parameter is near 1, then, the luminance is not filtered and is still reachable at the output, typicall value is 0
hcellsTemporalConstant(1.f),// the time constant of the first order low pass filter of the horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is frames, typical value is 1 frame, as the photoreceptors. Reduce to 0.5 to limit retina after effects.
hcellsSpatialConstant(7.f),//the spatial constant of the first order low pass filter of the horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, typical value is 5 pixel, this value is also used for local contrast computing when computing the local contrast adaptation at the ganglion cells level (Inner Plexiform Layer parvocellular channel model)
ganglionCellsSensitivity(0.7f)//the compression strengh of the ganglion cells local adaptation output, set a value between 0.6 and 1 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 0.7
normaliseOutput(true), //specifies if (true) output is rescaled between 0 and 255 of not (false)
parasolCells_beta(0.f), // the low pass filter gain used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), typical value is 0
parasolCells_tau(0.f), //the low pass filter time constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical value is 0 (immediate response)
parasolCells_k(7.f), //the low pass filter spatial constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical value is 5
amacrinCellsTemporalCutFrequency(1.2f), //the time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, typical value is 1.2
V0CompressionParameter(0.95f), the compression strengh of the ganglion cells local adaptation output, set a value between 0.6 and 1 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 0.95
localAdaptintegration_tau(0.f), // specifies the temporal constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
localAdaptintegration_k(7.f) // specifies the spatial constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
