Image Adjustment

Image adjustment generally includes level, contrast, gamma, hue, saturation and brightness modification.

In Matlab, you can use the func imadjust()% read the image
I = imread("./pout.tif");
imshow();
% Adjust the contrast the image so that 1% of the data is saturated at low and high intensities, and display it
J = imadjust(I);
figure
imshow(J)There is no builtin solution in OpenCV to perform it, but it can be easily realized in a loop.
#include <opencv2/opencv.hpp>
#include <vector>
#include <algorithm>

using namespace std;
using namespace cv;

void imadjust(const Mat1b& src, Mat1b& dst, int tol = 1, Vec2i in = Vec2i(0, 255), Vec2i out = Vec2i(0, 255))
{
// src: input CV_8UC1 image
// dst: output CV_8UC1 image
// tol: tolerance, from 0 to 100.
// in: src image bounds
// out: dst image bounds
dst = src.clone();

tol = max(0, min(100, tol));

if (tol > 0)
{
// Histogram
vector<int> hist(256, 0);
for (int r = 0; r < src.rows; r++) {
for (int c = 0; c < src.cols; c++) {
hist[src(r, c)]++;
}
}

// Cumulative histogram
vector<int> cum = hist;
for (int i = 1; i < hist.size(); i++)
cum[i] = cum[i - 1] + hist[i];

// Compute bounds
int total = src.rows * src.cols;
int low_bound = total * tol / 100;
int up_bound = total * (100 - tol) / 100;
in[0] = distance(cum.begin(), lower_bound(cum.begin(), cum.end(), low_bound));
in[1] = distance(cum.begin(), lower_bound(cum.begin(), cum.end(), up_bound));
}

// Stretching
float scale = float(out[1] - out[0]) / float(in[1] - in[0]);
for (int r = 0; r < dst.rows; r++) {
for (int c = 0; c < dst.cols; c++) {
int vs = max(src(r, c) - in[0], 0);
int vd = min(int(vs * scale + 0.5f) + out[0], out[1]);
dst(r, c) = saturate_cast<uchar>(vd);
}
}
}

int main()
{
char img_file[256] = "./imadjust1.png";

Mat3b img = imread(img_file);
if (!img.data) {
printf("Warning: the image [%s] is empty!\n", img_file);
}
return 0;

Mat1b gray;
cvtColor(img, gray, COLOR_RGB2GRAY);

Mat1b adjusted;
imadjust(gray, adjusted);

imwrite("./adjusted.png", adjusted);
return 0;
}