# Histogram Equalisation in C | Image Processing

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.

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 #include #include #include #include    // 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");         exit(1);     }        // creating output file that has write and read access     output_file = creat(output_file_name, 0666);     if (output_file < 0) {         printf("Error creating output file\n");         exit(1);     }        // 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++)             hist[(int)image[col]]++;     }        // calulating 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     close(input_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     free(image);        // closing input and output files     close(input_file);     close(output_file); }    // 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)

Transformation of Histogram before and after Equalisation (From Left to Right)

Note that boat image used in the program is a grayscale raw image. ImageJ and GNUplot are used for viewing images and plotting histograms.

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