官术网_书友最值得收藏!

Image color equalization

In this section, we are going to learn how to equalize a color image. Image equalization, or histogram equalization, tries to obtain a histogram with a uniform distribution of values. The result of equalization is an increase in the contrast of an image. Equalization allows lower local contrast areas to gain high contrast, spreading out the most frequent intensities. This method is very useful when the image is extremely dark or bright and there is a very small difference between the background and foreground. Using histogram equalization, we increase the contrast and the details that are over- or under-exposed. This technique is very useful in medical images, such as X-rays.

However, there are two main disadvantages to this method: the increase in background noise and a consequent decrease in useful signals. We can see the effect of equalization in the following photograph, and the histogram changes and spreads when increasing the image contrast:

Let's implement our equalization histogram; we are going to implement it in the Callback function defined in the user interface's code:

void equalizeCallback(int state, void* userData)
{ Mat result; // Convert BGR image to YCbCr Mat ycrcb; cvtColor(img, ycrcb, COLOR_BGR2YCrCb); // Split image into channels vector<Mat> channels; split(ycrcb, channels); // Equalize the Y channel only equalizeHist(channels[0], channels[0]); // Merge the result channels merge(channels, ycrcb); // Convert color ycrcb to BGR cvtColor(ycrcb, result, COLOR_YCrCb2BGR); // Show image imshow("Equalized", result); }

To equalize a color image, we only have to equalize the luminance channel. We can do this with each color channel but the result is not usable. Alternatively, we can use any other color image format, such as HSV or YCrCb, that separates the luminance component in an individual channel. Thus, we choose YCrCb and use the Y channel (luminance) to equalize. Then, we follow these steps:

1. Convert or input the BGR image into YCrCb using the cvtColor function:

Mat result; 
// Convert BGR image to YCbCr 
Mat ycrcb; 
cvtColor(img, ycrcb, COLOR_BGR2YCrCb); 

2. Split the YCrCb image into different channels matrix:

// Split image into channels 
vector<Mat> channels; 
split(ycrcb, channels); 

3. Equalize the histogram only in the Y channel, using the equalizeHist function which has only two parameters, the input and output matrices:

// Equalize the Y channel only 
equalizeHist(channels[0], channels[0]); 

4. Merge the resulting channels and convert them into the BGR format to show the user the result:

// Merge the result channels 
merge(channels, ycrcb); 
 
// Convert color ycrcb to BGR 
cvtColor(ycrcb, result, COLOR_YCrCb2BGR); 
 
// Show image 
imshow("Equalized", result);

The process applied to a low-contrast Lena image will have the following result:

主站蜘蛛池模板: 壶关县| 定远县| 永春县| 安庆市| 甘南县| 合阳县| 祁东县| 横峰县| 河北区| 曲靖市| 宣化县| 海南省| 南陵县| 康定县| 昭苏县| 马山县| 三门县| 陵水| 宣汉县| 滦平县| 海伦市| 洞口县| 泰来县| 桦川县| 栾川县| 玉山县| 五莲县| 汝州市| 上犹县| 慈利县| 梁平县| 沙洋县| 池州市| 卫辉市| 东丰县| 巴彦县| 屯留县| 当阳市| 石台县| 舟曲县| 兰坪|