Enhancing the Detail Resolution of Foggy Images Using Fuzzy Histogram Equalization with Weighted Distribution

Enhancing image quality is an essential step in developing computer vision since it can significantly increase the efficacy of other algorithms, such as object recognition. Image quality improvement has been done in many different sectors to recover and analyze various aspects. This study combines a weighted fuzzy histogram equalization method with other image quality improvement algorithms to rebuild images affected by inhomogeneous blur. This process begins by generating a phase dissimilarity histogram of the brightness of neighboring pixels to enhance contrast. Gamma correction is then used to boost the dark areas, and maximum saturation is used to prevent the fading effect. The proposed method was evaluated using the PSNR and SSIM indices. These indices are calculated for the results of the proposed method and compared to the previous images. The effectiveness of the proposed algorithm in reconstructing images and the degree to which the results closely resemble the original images will be determined by this comparison. This article also included a qualitative review, the results of which were discussed. Although this method generally sharpens visual details, it might not significantly increase scores in this particular scenario.

کلیدواژه ها:

Fuzzy Histogram Equalization ، Weighted distribution ، Foggy images ، quality improvement ، Gamma Correction ، Saturation maximization