Skip to main content

Opencv C++ tutorial : Smoothing, blur, noise reduction / canceling

Smooth or blur, gaussian blur, and noise-canceling, This tutorial will learn OpenCV blur, GaussianBlur, median blur functions in C++. Additionally, the advanced technique for noise reduction fastNlMeansDenoising family will be introduced with a code example for each method.  
opencv denoising

You can use blurring of the image to hide identity or reduce the noise of the image.  Blur can be a very useful operation and it is a very common operation as well. For example, the anonymization of pedestrians, face or is one possible target for blue operation. The blur is the most common task to perform over the image to reduce noise. The noise reduction is more task for Gaussian blur than for simple blur operation. The various blur operations are very common for image processing on mobile devices. 
The more important is the robustness issues of the data in pre-processing for machine learning. Sometimes, by blurring the images of the dataset can have a positive effect on the robustness of the achieved detector. This is for longer discussion because the chosen algorithm maters at the first place. The dataset blurring can bring surprisingly effect on the result and needs to be evaluated against the same ML algorithm.

To summarize this, Blur is used for example in the following situations. 

  • Anonymization 
  • Noise filtering 
  • Image preparation for machine learning  processing 
  • Image effects and processing 

Some basic mathematical for simple blur in OpenCV

Imagine just grayscale images, where the intensity is from 0 to 255. Our image has for simplicity just one row and 10 columns. Snake pixel. 
[0, 0, 0, 0, 0, 255, 255, 255, 255, 255]
There is a sharp transfer between the value 0 to 255. How to achieve this transfer much smoother that step from 0 to 255. The mean value of all 10 elements is first + second + another + .. + last divided by the number of all (10). We do not want to set all to the mean value 127 rounded down.
[127, 127, 127, 127, 127, 127, 127, 127, 127, 127]
What if we use the mean value of some selection. That can work. The mean value of three neighborhood elements that set the element in the middle.
[(0, (0, 0), (0), 0, 255), 255, 255, 255, 255]
This is mean of 0 + 0 +255 in blue bracket divided by 3. The fifth element is now 85. As our defined filter that calculates mean ower the image move one position to the right the sixth element is calculated as 0 + 255 + 255 divided by 3, This is 510 /3 =170. The next of our row will be the same as three times 255 divided three times is 255. Wait, what is our goal? Smooth the transition from 0 to 255. Great, goal is achieved and the edge is 0 85 170 255. This is a much smoother transition.
[(0, 0, 0),( 0, 85, 170), 255, 255, 255, 255]
This can be applied on images pixels as well, just more rows
[0, 0, 0,0, 85, 170, 255, 255, 255, 255;
0, 0, 0,0, 85, 170, 255, 255, 255, 255;
0, 0, 0,0, 85, 170, 255, 255, 255, 255]
This principle is called a normalized box filter. The most basic principle. The concrete pixel is the mean of the neighborhood. The window over is mean calculated is often called the kernel. Lets smooth the same row by mean of 5 neighborhood elements.
[(0, 0, 0, 0, 0), 255, 255, 255, 255, 255]
[0, (0, 0, 0, 0, 255), 255, 255, 255, 255]
255/5 = 51
[0, 0, (0, 0, 0, 255, 255), 255, 255, 255]
(2*255)/5 =102
(3*255)/5 =153
(4*255)/5 =204
(5*255)/5 =255
The filter of 3 neighborhood elements produces edge as 0 85 170 255. The filter over 5 neighborhood produce transfer edge 0 0 51 102 153 204 255 255 Much smoother transfer, right? This product image without the sharp edges. Let's say with fewer details as well. The great feature is that image is less noisy as well.The small detail, This simple math is performed in 2D blocks. 
image smooth

Opencv blur

The OpenCV blur function has simple parameters as blur(input image, output image, size of the kernel). The size of the kernel is of type Size(rows,cols).
blur(loadedPictureHere, processedPicture, Size(5 ,5));

Gaussian blur 

The Gaussian blur is the same mechanism as a blur, with one exception. The kernel represents the Gaussian distribution. Each pixel of the array over the calculated one is weighted by this distribution to produce output.

Opencv GaussianBlur

The OpenCV gaussian blur function has simple parameters as GaussianBlur(input image, output image, size of the kernel, the standard deviation in x, the standard deviation in y). The size of the kernel is of type Size(rows,cols). The standard deviation is from the statistics of the neighborhood pixel.
GaussianBlur(loadedPictureHere, processedPicture, Size(5 ,5),0,0);

Median blur 

It is doing exactly the operation represented by a median. It takes array over some value. Sort the array from the lower to higher element. Replace the value of the element which is in the middle of the sorted array. 

OpenCV median blur

The OpenCV median blur function has simple parameters as MedianBlur(input image, output image, size of the kernel). The size of the kernel is of type int.
medianBlur(loadedPictureHere, processedPicture, 5);

Code of blur, Gaussian blur,  and median blur

// Prepare new mat container 
    Mat loadedPictureHere; 
    //imread will read the image from file
    loadedPictureHere = imread("C:/Users/Vlada/Desktop/DNN/x64/Release/noise.png");

    Mat processedPicture;

    // blur is contro by size of the block Size(x,y) of moving windows
    blur(loadedPictureHere, processedPicture, Size(5 ,5)); <<<<< Changing kernel

    // prepare bigger Mat to display image and process image side by side (this Mat is bigger)
    Mat output = Mat((loadedPictureHere.cols * 3), loadedPictureHere.rows *2 ,

    // copy small images process and not processed into the bigger one. 
    loadedPictureHere.copyTo(output(Rect(00,  loadedPictureHere.cols,

    namedWindow("Step 1", WINDOW_AUTOSIZE);// Create a window for display.
    imshow("Step 1", output);   // Display loaded image 
blur(loadedPictureHere, processedPicture, Size(5 ,5));
GaussianBlur(loadedPictureHere, processedPicture, Size(5 ,5),0,0);
medianBlur(loadedPictureHere, processedPicture, 5);
blur opencv
GaussianBlur opencv
medianBlur opencvblur(loadedPictureHere, processedPicture, Size(10,10)); 
GaussianBlur(loadedPictureHere, processedPicture, Size(11 ,11),0,0);
medianBlur(loadedPictureHere, processedPicture, 11);
blur opencv
GaussianBlur opencv
medianBlurblur(loadedPictureHere, processedPicture, Size(15,15));
GaussianBlur(loadedPictureHere, processedPicture, Size(15,15),0,0);
medianBlur(loadedPictureHere, processedPicture, 15);
blur opencv
GaussianBlur opencv

Opencv advanced noise reduction fastNlMeansDenoising

This method fastNlMeansDenoising is based on an article Here. This method performs a search for average most similar pixels for one that should be denoise. These pixels can be far from the one that should be replaced. "The most similar pixels to given have no reason to be close at all". The search for such a pixel is not simple calculation over the neighborhood of such a pixel as in case of a blur, gaussian blur or median blur. 

fastNlMeansDenoising opencv denoising 

This function is to denoise the single grayscale image. The first parameter is input image and the second is processed image. 
fastNlMeansDenoising(loadedPictureHere, processedPicture,30721);
1 parameter input image
2 output image
3 The recommended value is 7, Size of template path to compute weights
4 The recommended value is 21, Size of windows used to compute weight average, This influence performance
5 smaller value preserves details but also preserves some noise

fastNlMeansDenoisingColored opencv denoising 

This function should be used for colored images
fastNlMeansDenoisingColored(loadedPictureHere, processedPicture,307, 3,10);
1 parameter input image
2 output image
3 The recommended value is 7, Size of template path to compute weights
4 The recommended value is 21, Size of windows used to compute weight average, This influence
5 smaller value preserves details but also preserves some noise for gray
6 smaller value preserves details but also preserves some noise for colors. The 10 is the recommended value to remove color noise. 

fastNlMeansDenoisingColoredMulti OpenCV denoising 

This will perform the denoise for one image but find the best pixels value in the buffer of images. It is like the iPhone takes 3 images and combine this sequence into one perfect picture. 

vector buffer(5);
Mat image;

buffer[0] = image;
buffer[1] = image;
buffer[2] = image;
buffer[3] = image;
buffer[4] = image;
Mat output;
fastNlMeansDenoisingColoredMulti(buffer, output, 5, imgs_count, 1248);
1 parameter input image buffer
2 output image
3 index of the image in the buffer to be denoised
4 Temporal windows size is number of surrounding images. imgs_count
XXX This two can be skipped
5 smaller value preserves details but also preserves some noise for gray
6 smaller value preserves details but also preserves some noise for colors. The 10 is the recommended value to remove color noise.
7 The recommended value is 7, Size of template path to compute weights
8 The recommended value is 21, Size of windows used to compute weight average, This influence

Tutorial code for basic blur and fastNlMeansDenoising

#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/video.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/core.hpp>
#include <opencv2/imgcodecs.hpp>

#include <opencv2/photo.hpp>
using namespace cv;
using namespace std;

int main()

    // Prepare new mat container 
    Mat loadedPictureHere; 
    //imread will read the image from file
    loadedPictureHere = imread("C:/Users/Vlada/Desktop/DNN/x64/Release/noise.png");

    Mat processedPicture;

    // You can use one of these 
    //blur(loadedPictureHere, processedPicture, Size(10,10)); 
    //GaussianBlur(loadedPictureHere, processedPicture, Size(11 ,11),0,0);
    //medianBlur(loadedPictureHere, processedPicture, 11);
    medianBlur(loadedPictureHere, processedPicture, 15);
    // prepare bigger Mat to display image and process image side by side (this Mat      //is bigger)
    Mat output = Mat((loadedPictureHere.cols * 3), loadedPictureHere.rows *2 ,

    // copy small images process and not processed into the bigger one. 
    loadedPictureHere.copyTo(output(Rect(00,  loadedPictureHere.cols,

    namedWindow("Step 1", WINDOW_AUTOSIZE);// Create a window for display.
    imshow("Step 1", output);   // Display loaded image 

    fastNlMeansDenoising(loadedPictureHere, processedPicture,30721);
    //fastNlMeansDenoisingColored(loadedPictureHere, processedPicture, 7, 100); 

    // copy small images process and not processed into the bigger one. 

    namedWindow("Step 2", WINDOW_AUTOSIZE);// Create a window for display.
    imshow("Step 2", output);   // Display loaded image 
return 0


Popular posts from this blog

Opencv 3.1 Tutorial Optical flow (calcOpticalFlowFarneback)

Farneback Optical flow Opencv simple C++ tutorial and code to achieve optical flow and farneback optical flow of moving an object in opencv video. Lets check the video example and the achieved result on my blog. Working and well describe code is included. 

Optical Flow Farneback parameters remarksYou need 2 images at least to calculate optical flow, the previous image (prevgray) and current image (img).  !! The previous image must be initialized first !!  Both images have to be gray scale. 
Result is computer in flowUmat which has same size as inputs but format is CV_32FC2

0.4- image pyramid or simple image scale
1 is number of pyramid layers. 1 mean that flow is calculated only from previous image.  12 is win size.. Flow is computed over the window larger value is more robust to the noise.  2 mean number of iteration of algorithm 8 is polynomial degree expansion recommended value are 5 - 7 1.2 standard deviation used to smooth used derivatives recommended values from 1.1 - 1,5

Fast Opencv people pedestrian detection Tutorial by CascadeClassifier

Simple Opencv C++ tutorial and example of people detection in video samples and pictures. There is nothing you cannot achieve in few simple steps. People detection and performace tasks in opencv could be solved if you have little bit knowledge of programing. Or just simple follow the steps..  Opencv tutorial instalation of opencv You can simple prepare the project inside the Visual Studio 2015 by Nuget Packages. This approach is easy for beginers and better than standard installation with all the environmental variables problems. Just follow the installation steps inside here 

Opencv is great and complex tools. There is lot of image processing and also machine learning features. You can simply learn your own detector. I would like to prepare some tutorial how to learn your own detector. It is long time run. 
All, you need to do, is some experience and basic opencv tools.  under opencv/build/x64/vc14/bin
Prepare your dataset and files w…

Install opencv Visual Studio 2015

Install opencv for Visual Studio 2015  Opencv tutorial how to build opencv from source in Visual Studio 2015. This is usefull when the new version just release and there is no prebuild library awailable..  If you download prebuild libs for windows Visual studio some times agou there is problem the newest VS just mussing. Lets checkt the version of libraries and VS.
Prebuild libs are only for version VC11 and VC12. This mean Visual Studio 2012 and 2013.

This step helps you compile your own opencv libs for VC14  - Visual Studio 2015 Community edition.

Important !!
 Now a days just use NUGET packages in Visual studio and you can code under 1 minutes. here.

Prepare third party libs for opencv  This step depends on your requirements. If you want python lets install python. But i can reccomend to install following parts. 
Intel © Threading Building Blocks (TBB)Intel © Integrated Performance Primitives (IPP) Build opencv 3.0.0  Download Opencv 3.0.0 gold…

Opencv C++ Tutorial, Mat Roi, Region of interest

Opencv ROI, Region of Interest Simple opencv C++ tutorial how to work with ROI. Code example about selecting the rectangle region of interest inside the image and cut or display part of the image from the bigger picture. There is nothing what is difficult about this. Only trick is about one line of code. 
Rect RectangleToSelect(x,y,width,height) Mat source; Mat roiImage = source(RectangleToSelect);
This is first post from this series. This simple opencv tutorials are all over the web. I would like to visualize all my steps through the code and //comment them. Each tutorial will contain small amount of step to keep reader focused.  First tutorial about mat resizeing is available under that link Mat Resize
I am using Visual Studio 2015,  How to use Opencv 3.0.0 with Visual Studio can be found here install opencv visual studio 2015. In Visual studio 2015 is best options to use NUGET packages, Here is described how to install Opencv by NUGET. It is easy. Working under one minute after you f…

Opencv VideoCapture File, Web Camera, RTSP stream

Opencv VideoCapture File, Camera and stream Opencv tutorial simple code in C++ to capture video from File, Ip camera stream and also the web camera plug into the computer. The key is to have installed the FFMPEG especially in case of reading the stream of IP cameras. In windows just use Opencv Installation by Nugets packages Here. Simple easy under 2 minutes installation. In Linux you need to follow the instruction below. If you are on Debian Like package system. Under Fedora Red hat dist just use a different approach. Code is simple and installation is the key.. 
Windows use nugets packages Linux you have to install and build Opencv With FFMPEG. Also simple.  It is easy to capture video in OpenCV Video capture
 in OpenCV is a really easy task, but for a little bit experienced user. 
What is the problem?
The problem is the installation of Opencv without recommended dependencies.

Just install all basic libs that are recommended on the website.
# Basic packagessudo apt-get -y install …

Opencv reading IP camera, Video stream, Web camera, images and

Opencv reading video files, reading video stream, Images, IP and Web cameras. I would like to cover this all in one post. Yes, video writer is also important to store your results and achievements in video. There is couple of simple trick and if you follow them, you will never have a problem with the reading and writing video, stream, files in future. Basic opencv web camera reading There is couple think you need to take care. My favorite installation on windows platform is trough NUGET package system. It is easy in few steps. I describe this many times for example VS 2017 here. Nuget set up your project without any linking settings, library path selection, global environmental variables and you can directly start coding in few seconds. Just select and install nuget and compile code below. Nothing else.  You need to take care if you have included several thinks. highgui.hpp core.hpp, imgproc.hpp, videoio, imgcodecs. All of them are not necessary to read the web camera but for example…

Opencv build and install Visual Studio 2017 Contrib library

Easy install and build of Opencv 3+ tested on 3.2 version with contributor library and additional features described step by step, picture by picture. After this tutorial you can modify setting of CMAKE project according to HW possibilities and available libraries to build your own Opencv library. Most of the time, Prebuild libs with already generated DLL, LIBS are used to start project and coding. In case, that new visual studio 2017 is available there is no prebuild libraries for VS141, Thich is from my point of view confusing naming of Libraries compatible with Visual Studio 2017. Opencv VS 2017 install options Alternatives to this tutorial. You can skip this.  There is possibility use some compatibility pack downloaded to VS140 and use same prebuild library as in case of Visual Studio 2015 this is described hereThe second way is to try use some prebuild NUGET package. I am using nugets a lot. Simple installation under one line of code inside nuget packages console. here
Opencv Ins…

Head and people detection in opencv

LBP cascade for detect head and people in opencv  LBP cascade free to download to use in opencv to detect people and heads. Code example and cascade description. All you need to write your own people head detector from the youtube video.
Cascade is trained on my own people and head datasets. There are no perfect but in some cases are better then default opencv cascades. They are just different.. For example you can count that the head detector have much more false detection than the people detector.. The shape and feature space is much more common and close to others shapes than the whole people detector.

Issues with opencv detectMultiScale head and people detector Please let me know if cascades worked as expected.. In code example there is ground threshold settings and reccomentation. 

LBP cascade head detection properties Sure you can find inside file.
<!-- This is just basic 16 stage lbp cascade head detector develop by  V.K. from --> <?xml …

Opencv C++ Tutorial Mat resize

Opencv Mat Resize   Resize the Mat or Image in the Opencv C++ tutorial. It is obviously simple task and important to learn. This tutorial is visualized step by step and well-described each of them. The main trick is in that simple code.

Mat Input;
Mat Resized;
int ColumnOfNewImage = 60;
int RowsOfNewImage = 60;
resize(Input, Resized, Size(ColumnOfNewImage,RowsOfNewImage));

This code just takes an Input image and resized save to output Mat. How big is the resized image is based on the Size? Size just contains two parameters. Simple numbers of how the result should be big. The simple number of columns (width) and rows (height). That is basically it. Enjoy

                                                Boring same face again and again. 

Load Image, resize and save Opencv C++ #include <Windows.h> #include "opencv2\highgui.hpp" #include "opencv2\imgproc.hpp" #include "opencv2\video\background_segm.hpp" #include "opencv2\video\tracking.hpp"
using n…

Opencv 3.1 people detection by CascadeClassifier

People detection by Cascade Classifier Opencv The comparison of Opencv Cascade for people detections. Default opencv cascades are good one. You can simply achive better result but you need to collect the training data. On my blog you can find the datasets for car detection. There is more than 2000 positives car samples available for you and test to learn your own detector..  Here in this article, I just want to show my cascade compare to cascade which are default in opencv.  LBP cascade for people detection Also I recently publish LBP cascade for people detection. You can download 12 stage LBP cascade LBP cascade for download. There is couple or maybe more than 3 tutorials how to use detect multiscale in opencv. Just download the LBP cascade and enjoy coding in Opencv..

Haar Cascade Classifier for People detection In this short tutorial, I would like to test some standard haar cascade in opencv 3.1.  My first note is where you can find this cascades. When you download opencv for wind…