Fuzzy color histogram equalization with weighted distribution for image enhancement

Low light often degrades the quality of an image, which in turn affects the computer vision algorithm's performance. The development of image enhancement algorithms improves the visual quality and valuable details of an image. Nevertheless, the existing algorithms inevitably introduce unwanted artifacts and color distortion while preserving the tones and information. Implementing a new fuzzy color histogram equalization with a weighted distribution algorithm will address these drawbacks effectively and yields a better-quality image. In the proposed method, fuzzy dissimilarity histogram is constructed from the neighbourhood characteristics of an intensity to improve the contrast and naturalness of an image. Then, incorporate gamma correction for further enhancement in dark regions. Finally, modify the saturation to the permittable maximum saturation range to avoid the fading effect. The extended experimental results on different scenes demonstrate that the proposed algorithm can enhance the quality and details of an image efficiently. Objective measures show the competitive performance of the proposed algorithm compared with the other existing methods.