Texture enhanced underwater image restoration via Laplacian regularization

Underwater images are usually degraded by color distortion, blur, and low contrast due to the fact that the light is inevitably absorbed and scattered when traveling through water. The captured images with poor quality may greatly limit their applications. To address these problems, we propose a new Laplacian variation model based on underwater image formation model and the information derived from the transmission map and background light. Technically, a novel fidelity term is designed to constrain the radiance scene, and a divergence-based regularization is applied to strengthen the structure and texture details. Moreover, the brightness-aware blending algorithm and quad-tree subdivision scheme are integrated into our variational framework to perform the transmission map and background light estimation. Accordingly, we provide a fast-iterative algorithm based on the alternating direction method of multipliers to solve the optimization problem and accelerate its convergence speed. Experimental results demonstrate that the proposed method achieves outstanding performance on dehazing, detail preserving, and texture enhancement for improving underwater image quality. Extensive qualitative and quantitative comparisons with several state-of-the-art methods also validate the superiority of our proposed method. The code is available at: https://github.com/Hou-Guojia/ULV.