Gamma Correction Algorithm Implementation in MATLAB

This MATLAB implementation demonstrates the gamma correction algorithm designed to address uneven illumination in images. Gamma correction is a fundamental image processing technique that adjusts image contrast and brightness based on luminance distribution characteristics. By modifying the gamma parameter value, the algorithm alters the image's light-dark relationships, making subtle details more discernible. In images with uneven lighting conditions, certain regions often appear darker or brighter than others. The gamma correction algorithm effectively normalizes these variations by applying a non-linear transformation to pixel intensities. The core mathematical operation involves: output = input^gamma, where input represents normalized pixel values (0-1 range) and gamma controls the correction intensity. Key implementation aspects include: - Image data normalization to [0,1] range using im2double function - Gamma value optimization through histogram analysis - Power-law transformation using element-wise operations (.^ operator) - Output scaling back to standard image data ranges The MATLAB implementation provides a flexible framework for parameter tuning, allowing users to experimentally determine optimal gamma values for specific image characteristics. This approach significantly enhances image quality and visual clarity, particularly beneficial for preprocessing images prior to further computer vision applications. Gamma values below 1 enhance darker regions (increasing visibility in shadows), while values above 1 brighten already-light areas. The algorithm's efficiency makes it suitable for batch processing multiple images with consistent lighting issues.