Adaptive Gamma Correction of Subregion for Non-Uniform Illumination Image Enhancement

This paper proposes an improved adaptive two-dimensional gamma correction method based on the illumination component and target mean value to address the issue of over-enhancement in nonuniformly illuminated images. The process begins with the conversion of images to the HSV space, from which the V-channel image is extracted for processing. Utilizing the illumination-reflection model, the illumination component is estimated through a guided image filter with good edge retention. Concurrently, the V-channel image region is segmented into bright and dark regions, and a target mean function with varying adjustment coefficients is established. The illumination component and adaptive target mean value are used to act on the gamma function for two-dimensional gamma correction, and histogram equalization is subsequently performed. The final output is obtained by merging V-channel component with the H and S channels and converting it back to the RGB space. Experimental evaluations on DICM and LIME datasets reveal that in comparison to four typical enhancement algorithms, the proposed algorithm achieves an average increase of 10. 6% in information entropy, 97. 5% in mean gradient (MG), and 77. 8% in signal-to-noise ratio (SNR), with an average processing time of 0. 32 s. These enhancements significantly improve the visual quality of images, making them more suitable for machine vision research. The proposed algorithm offers advantages in terms of high real-time performance and simplicity and produces output images with more natural colors, uniform brightness, clearer details, and an overall enhanced visual effect.