diff --git a/doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown b/doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown
index 83fef32f33..feb1e0985e 100644
--- a/doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown
+++ b/doc/tutorials/dnn/dnn_googlenet/dnn_googlenet.markdown
@@ -37,11 +37,7 @@ Explanation
 
 -# Read input image and convert to the blob, acceptable by GoogleNet
    @snippet dnn/caffe_googlenet.cpp Prepare blob
-   Firstly, we resize the image and change its channel sequence order.
-
-   Now image is actually a 3-dimensional array with 224x224x3 shape.
-
-   Next, we convert the image to 4-dimensional blob (so-called batch) with 1x3x224x224 shape by using special cv::dnn::blobFromImages constructor.
+   We convert the image to a 4-dimensional blob (so-called batch) with 1x3x224x224 shape after applying necessary pre-processing like resizing and mean subtraction using cv::dnn::blobFromImage constructor.
 
 -# Pass the blob to the network
    @snippet dnn/caffe_googlenet.cpp Set input blob
diff --git a/samples/dnn/squeezenet_halide.cpp b/samples/dnn/squeezenet_halide.cpp
index 6de90cbfce..f79c146df2 100644
--- a/samples/dnn/squeezenet_halide.cpp
+++ b/samples/dnn/squeezenet_halide.cpp
@@ -79,8 +79,7 @@ int main(int argc, char **argv)
         exit(-1);
     }
 
-    resize(img, img, Size(227, 227));                // SqueezeNet v1.1 predict class by 3x227x227 input image.
-    Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(), false);  // Convert Mat to 4-dimensional batch.
+    Mat inputBlob = blobFromImage(img, 1.0, Size(227, 227), Scalar(), false, false);  // Convert Mat to 4-dimensional batch.
     //! [Prepare blob]
 
     //! [Set input blob]
diff --git a/samples/dnn/ssd_object_detection.cpp b/samples/dnn/ssd_object_detection.cpp
index 3f89510f9f..d88d65498e 100644
--- a/samples/dnn/ssd_object_detection.cpp
+++ b/samples/dnn/ssd_object_detection.cpp
@@ -10,36 +10,6 @@ using namespace cv::dnn;
 #include <cstdlib>
 using namespace std;
 
-const size_t width = 300;
-const size_t height = 300;
-
-static Mat getMean(const size_t& imageHeight, const size_t& imageWidth)
-{
-    Mat mean;
-
-    const int meanValues[3] = {104, 117, 123};
-    vector<Mat> meanChannels;
-    for(int i = 0; i < 3; i++)
-    {
-        Mat channel((int)imageHeight, (int)imageWidth, CV_32F, Scalar(meanValues[i]));
-        meanChannels.push_back(channel);
-    }
-    cv::merge(meanChannels, mean);
-    return mean;
-}
-
-static Mat preprocess(const Mat& frame)
-{
-    Mat preprocessed;
-    frame.convertTo(preprocessed, CV_32F);
-    resize(preprocessed, preprocessed, Size(width, height)); //SSD accepts 300x300 RGB-images
-
-    Mat mean = getMean(width, height);
-    cv::subtract(preprocessed, mean, preprocessed);
-
-    return preprocessed;
-}
-
 const char* classNames[] = {"background",
                             "aeroplane", "bicycle", "bird", "boat",
                             "bottle", "bus", "car", "cat", "chair",
@@ -126,9 +96,7 @@ int main(int argc, char** argv)
             cvtColor(frame, frame, COLOR_BGRA2BGR);
 
         //! [Prepare blob]
-        Mat preprocessedFrame = preprocess(frame);
-
-        Mat inputBlob = blobFromImage(preprocessedFrame, 1.0f, Size(), Scalar(), false); //Convert Mat to batch of images
+        Mat inputBlob = blobFromImage(frame, 1.0f, Size(300, 300), Scalar(104, 117, 123), false, false); //Convert Mat to batch of images
         //! [Prepare blob]
 
         //! [Set input blob]
diff --git a/samples/dnn/tf_inception.cpp b/samples/dnn/tf_inception.cpp
index f184d39270..e411cc8519 100644
--- a/samples/dnn/tf_inception.cpp
+++ b/samples/dnn/tf_inception.cpp
@@ -78,12 +78,7 @@ int main(int argc, char **argv)
         exit(-1);
     }
 
-    cv::Size inputImgSize = cv::Size(224, 224);
-
-    if (inputImgSize != img.size())
-        resize(img, img, inputImgSize);       //Resize image to input size
-
-    Mat inputBlob = blobFromImage(img);   //Convert Mat to image batch
+    Mat inputBlob = blobFromImage(img, 1.0f, Size(224, 224), Scalar(), true, false);   //Convert Mat to batch of images
     //! [Prepare blob]
     inputBlob -= 117.0;
     //! [Set input blob]
diff --git a/samples/dnn/torch_enet.cpp b/samples/dnn/torch_enet.cpp
index 6101d17f06..37a5db8da4 100644
--- a/samples/dnn/torch_enet.cpp
+++ b/samples/dnn/torch_enet.cpp
@@ -76,7 +76,7 @@ int main(int argc, char **argv)
         exit(-1);
     }
 
-    Mat inputBlob = blobFromImage(img, 1./255, Size(1024, 512), Scalar(), true, false);   //Convert Mat to image batch
+    Mat inputBlob = blobFromImage(img, 1./255, Size(1024, 512), Scalar(), true, false);   //Convert Mat to batch of images
     //! [Prepare blob]
 
     //! [Set input blob]
diff --git a/samples/dnn/yolo_object_detection.cpp b/samples/dnn/yolo_object_detection.cpp
index 3bc277898f..9740fbac40 100644
--- a/samples/dnn/yolo_object_detection.cpp
+++ b/samples/dnn/yolo_object_detection.cpp
@@ -14,9 +14,6 @@ using namespace std;
 using namespace cv;
 using namespace cv::dnn;
 
-const size_t network_width = 416;
-const size_t network_height = 416;
-
 static const char* about =
 "This sample uses You only look once (YOLO)-Detector (https://arxiv.org/abs/1612.08242) to detect objects on camera/video/image.\n"
 "Models can be downloaded here: https://pjreddie.com/darknet/yolo/\n"
@@ -104,13 +101,8 @@ int main(int argc, char** argv)
         if (frame.channels() == 4)
             cvtColor(frame, frame, COLOR_BGRA2BGR);
 
-        //! [Resizing without keeping aspect ratio]
-        Mat resized;
-        resize(frame, resized, Size(network_width, network_height));
-        //! [Resizing without keeping aspect ratio]
-
         //! [Prepare blob]
-        Mat inputBlob = blobFromImage(resized, 1 / 255.F); //Convert Mat to batch of images
+        Mat inputBlob = blobFromImage(frame, 1 / 255.F, Size(416, 416), Scalar(), true, false); //Convert Mat to batch of images
         //! [Prepare blob]
 
         //! [Set input blob]