Underwater Image Enhancement via Wavelet Decomposition Fusion of Advantage Contrast

Weidong Zhang, Qingmin Liu, Huimin Lu, Jianping Wang, Jing Liang

Abstract: Underwater images encounter a range of quality degradation issues caused by the differential scattering and absorption of light in water. To address these challenges, we introduce a WFAC method, a wavelet decomposition fusion method that combines global and local contrast for underwater image enhancement. Specifically, we begin with a color transfer compensation strategy to correct the colors in a degraded underwater image. Subsequently, we utilize the pixel gradient distribution to create a matrix weight map that dynamically adjusts the weight distribution in overly bright or dark areas of the color-corrected image, enhancing its global contrast. Simultaneously, we apply a rapid integration statistical strategy to adaptively fine-tune the local contrast of color-corrected images using the local mean and variance statistics. To combine the strengths of various enhanced images, we implement a wavelet decomposition fusion strategy to break down different scale components of globally and locally contrast-enhanced images and merge the benefits of varying scale images to obtain a high-quality underwater image. Comprehensive experimental assessments across three underwater image datasets demonstrate that our WFAC method efficiently recovers colors and boosts contrast in degraded underwater images. The code is publicly available at: https://www.researchgate.net/publication/386508762_2024WFAC.

External IDs:dblp:journals/tcsv/ZhangLLWL25