histogram equalization of grayscale and color image
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This document discusses histogram equalization of grayscale and color images using OpenCV. Histogram equalization is used to enhance contrast in images. It describes how histograms plot the distribution of pixel intensities in an image. The document provides OpenCV code to equalize histograms of grayscale and color images. It shows the original and equalized images, with the equalized images demonstrating enhanced contrast compared to the originals.
![Histogram Equalization of Grayscale and Color
Image
Mr. Saeed Ullah
Korean Institute of Science & Technology Information,
University of Science & Technology,
Daejeon, South Korea
Saeedonline12@gmail.com
Indra Kumari
Electronics and Telecommunication Research Institute,
University of Science & Technology,
Daejeon, South Korea
Kumariindra7@etri.re.kr
Summary—This report presents the Histogram Equalization of
Grayscale and Color Image. Tools used in this project are OpenCV
3.2 Library which is a library used for Computer Vision, and Visual
Studio 2015 (64 bit).
I. INTRODUCTION
Histogram is the intensity distribution of an image.
Histograms plots how many times (frequency) each intensity
value in image occurs. By looking at the histogram for a
specific image a viewer will be able to judge the entire tonal
distribution at a glance. A Histogram has two axis the x axis
and the y axis. The x axis contains event whose frequency you
have to count. The y axis contains frequency. An image
histogram is a graphical representation of the number of pixels
in an image as a function of their intensity. Histogram
equalization is used to enhance contrast. It is not necessary that
contrast will always be increase in this. There may be some
cases were histogram equalization can be worse. In that cases
the contrast is decreased.
Consider the following image. Say, depth of the image is 2 bits.
Therefore the value range for each and every pixel is from 0 to
3.
Fig.1. Sample Image (Depth = 2 bits)
Histogram of the image shows how the pixel values are
distributed. As in the above image there are 5 pixels with value
0, 7 pixels with value 1, 9 pixels with value 2 and 4 pixels with
value 3. These information is tabulated as follows.
Fig.2. Intensity Distribution of sample image
Histogram of an image usually presented as a graph. The
following graph represents the histogram of the above image.
Fig.3. Image Histogram
There are various techniques to achieve histogram equalization.
In OpenCV, there is a built OpenCV function to equalize
histogram.
II. OPENCV CODE
i) For Grayscale Image
Mat img = imread("..//data//green.JPG",
CV_LOAD_IMAGE_GRAYSCALE); //open and read the
image in Grayscale Format
Mat img_hist_equalized;
equalizeHist(img, img_hist_equalized);
//equalize the histogram
ii) For Colored Image
Mat img = imread("../data/green.jpg",
CV_LOAD_IMAGE_COLOR); //open and read the
Colored image.
vector<Mat> channels;
Mat img_hist_equalized;
cvtColor(img, img_hist_equalized, CV_BGR2YCrCb);
//change the color image from BGR to YCrCb
format
split(img_hist_equalized, channels); //split the
image into channels
equalizeHist(channels[0], channels[0]);
//equalize histogram on the 1st channel (Y)](https://image.slidesharecdn.com/2-200616131638/85/histogram-equalization-of-grayscale-and-color-image-1-320.jpg)
histogram equalization of grayscale and color image
- 1. Histogram Equalization of Grayscale and Color Image Mr. Saeed Ullah Korean Institute of Science & Technology Information, University of Science & Technology, Daejeon, South Korea Saeedonline12@gmail.com Indra Kumari Electronics and Telecommunication Research Institute, University of Science & Technology, Daejeon, South Korea Kumariindra7@etri.re.kr Summary—This report presents the Histogram Equalization of Grayscale and Color Image. Tools used in this project are OpenCV 3.2 Library which is a library used for Computer Vision, and Visual Studio 2015 (64 bit). I. INTRODUCTION Histogram is the intensity distribution of an image. Histograms plots how many times (frequency) each intensity value in image occurs. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance. A Histogram has two axis the x axis and the y axis. The x axis contains event whose frequency you have to count. The y axis contains frequency. An image histogram is a graphical representation of the number of pixels in an image as a function of their intensity. Histogram equalization is used to enhance contrast. It is not necessary that contrast will always be increase in this. There may be some cases were histogram equalization can be worse. In that cases the contrast is decreased. Consider the following image. Say, depth of the image is 2 bits. Therefore the value range for each and every pixel is from 0 to 3. Fig.1. Sample Image (Depth = 2 bits) Histogram of the image shows how the pixel values are distributed. As in the above image there are 5 pixels with value 0, 7 pixels with value 1, 9 pixels with value 2 and 4 pixels with value 3. These information is tabulated as follows. Fig.2. Intensity Distribution of sample image Histogram of an image usually presented as a graph. The following graph represents the histogram of the above image. Fig.3. Image Histogram There are various techniques to achieve histogram equalization. In OpenCV, there is a built OpenCV function to equalize histogram. II. OPENCV CODE i) For Grayscale Image Mat img = imread("..//data//green.JPG", CV_LOAD_IMAGE_GRAYSCALE); //open and read the image in Grayscale Format Mat img_hist_equalized; equalizeHist(img, img_hist_equalized); //equalize the histogram ii) For Colored Image Mat img = imread("../data/green.jpg", CV_LOAD_IMAGE_COLOR); //open and read the Colored image. vector<Mat> channels; Mat img_hist_equalized; cvtColor(img, img_hist_equalized, CV_BGR2YCrCb); //change the color image from BGR to YCrCb format split(img_hist_equalized, channels); //split the image into channels equalizeHist(channels[0], channels[0]); //equalize histogram on the 1st channel (Y)
- 2. merge(channels, img_hist_equalized); //merge 3 channels including the modified 1st channel into one image cvtColor(img_hist_equalized, img_hist_equalized, CV_YCrCb2BGR); //change the color image from YCrCb to BGR format (to display image properly) III. RESULTS Fig. 4. Original Image Fig. 5. Histogram Equalized Image Fig. 6. Original Image Fig. 7. Histogram Equalized Image CONCLUSION: - OpenCV provides many built-in functions for Histograms and Histogram Equalizations, in which some of the basic functions along with the source code and implementation on images were discussed in this report.