Optimized dehazing algorithm based on dark channel prior with Gabor filter and multiscale minimum filter

Abstract Traditional dehazing algorithms have long been limited in their ability to remove fog effectively, especially in preserving details. This study proposes an improved dark channel prior dehazing algorithm designed to overcome the limitations of traditional algorithms by refining the transmission map and estimating atmospheric light intensity. The algorithm fully exploits the anti‐haze characteristics of the Gabor filter to extract multi‐directional texture features. By fusing these features to form a guidance map for guided filtering, it effectively reduces the blurring caused by guided filtering on the image edges, thereby producing a more accurate transmission map. Simultaneously, an enhanced atmospheric light successfully reduces interference from white objects in the image. The experiment phase utilized the publicly available RESIDE dataset for validation. The algorithm achieved a Peak Signal‐to‐Noise Ratio (PSNR) of 22.4 and a Structural Similarity Index (SSIM) of 0.88. These metrics indicate the algorithm's superior dehazing capabilities.