Open In App
Related Articles

Histogram Equalisation in C | Image Processing

Improve Article
Save Article
Like Article

A histogram of a digital image represents intensity distribution by plotting bar graph with X-axis as pixel intensity value and Y-axis as the frequency of its occurrence. Histogram Equalisation is a technique to adjust contrast levels and expand the intensity range in a digital image. Thus, it enhances the image which makes information extraction and further image processing easier. Following is the algorithm to do histogram equalisation in C language.

  1. Convert the input image into a grayscale image
  2. Find frequency of occurrence for each pixel value i.e. histogram of an image (values lie in the range [0, 255] for any grayscale image)
  3. Calculate Cumulative frequency of all pixel values
  4. Divide the cumulative frequencies by total number of pixels and multiply them by maximum graycount (pixel value) in the image

For example, consider an image having total 25 pixels having 8 distinct pixel values. All the steps have been applied to the histogram of the original image. Image explaining steps of Histogram Equalisation The last row in the above image shows result after multiplication which is actually histogram equalised new gray level mapping of original gray levels. Below is the C program to perform histogram equalisation of an image. 


// C program to perform histogram equalisation to adjust contrast levels
// All the needed library functions for this program
#include <fcntl.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
// Function to perform histogram equalisation on an image
// Function takes total rows, columns, input file name and output
// file name as parameters
void histogramEqualisation(int cols, int rows,
                           char* input_file_name, char* output_file_name)
    // creating image pointer
    unsigned char* image;
    // Declaring 2 arrays for storing histogram values (frequencies) and
    // new gray level values (newly mapped pixel values as per algorithm)
    int hist[256] = { 0 };
    int new_gray_level[256] = { 0 };
    // Declaring other important variables
    int input_file, output_file, col, row, total, curr, i;
    // allocating image array the size equivalent to number of columns
    // of the image to read one row of an image at a time
    image = (unsigned char*)calloc(cols, sizeof(unsigned char));
    // opening input file in Read Only Mode
    input_file = open(input_file_name, O_RDONLY);
    if (input_file < 0) {
        printf("Error opening input file\n");
    // creating output file that has write and read access
    output_file = create(output_file_name, 0666);
    if (output_file < 0) {
        printf("Error creating output file\n");
    // Calculating frequency of occurrence for all pixel values
    for (row = 0; row < rows; row++) {
        // reading a row of image
        read(input_file, &image[0], cols * sizeof(unsigned char));
        // logic for calculating histogram
        for (col = 0; col < cols; col++)
    // calculating total number of pixels
    total = cols * rows;
    curr = 0;
    // calculating cumulative frequency and new gray levels
    for (i = 0; i < 256; i++) {
        // cumulative frequency
        curr += hist[i];
        // calculating new gray level after multiplying by
        // maximum gray count which is 255 and dividing by
        // total number of pixels
        new_gray_level[i] = round((((float)curr) * 255) / total);
    // closing file
    // reopening file in Read Only Mode
    input_file = open(input_file_name, O_RDONLY);
    // performing histogram equalisation by mapping new gray levels
    for (row = 0; row < rows; row++) {
        // reading a row of image
        read(input_file, &image[0], cols * sizeof(unsigned char));
        // mapping to new gray level values
        for (col = 0; col < cols; col++)
            image[col] = (unsigned char)new_gray_level[image[col]];
        // reading new gray level mapped row of image
        write(output_file, &image[0], cols * sizeof(unsigned char));
    // freeing dynamically allocated memory
    // closing input and output files
// driver code
int main()
    // declaring variables
    char* input_file_name;
    char* output_file_name;
    int cols, rows;
    // defining number of rows and columns in an image
    // here, image size is 512*512
    cols = 512;
    rows = 512;
    // defining input file name (input image name)
    // this boat_512_512 is a raw grayscale image
    input_file_name = "boat_512_512";
    // defining output file name (output image name)
    output_file_name = "boat_512_512_histogram_equalised";
    // calling function to do histogram equalisation
    histogramEqualisation(cols, rows, input_file_name, output_file_name);
    return 0;

Results: Boat image before and after Histogram Equalisation (From Left to Right) Image of Boat before and after Histogram Equalisation Transformation of Histogram before and after Equalisation (From Left to Right) Transformation of Histogram after applying Equalisation Note that boat image used in the program is a grayscale raw image. ImageJ and GNUplot are used for viewing images and plotting histograms.

Last Updated : 20 Mar, 2023
Like Article
Save Article
Similar Reads