Underwater image enhancement based on optimally weighted histogram framework and improved Fick's law algorithm.

The quality of underwater images is often affected by light scattering and attenuation, resulting in a loss of contrast and brightness. To address this issue, this paper proposes an underwater image enhancement method: improved Fick's law algorithm-based optimally weighted histogram framework (IFLAHF). The method incorporates the bi-histogram equalization-based three plateau limits (BHE3PL) technique to enhance image contrast and details while maintaining brightness. However, its dependence on fixed parameters limits its adaptability. To overcome this limitation, the paper introduces Fick's law algorithm (FLA) and then improves it to optimize the fixed parameters. FLA is improved by incorporating Tent chaotic mapping and reverse learning to increase population diversity, and Levy flight is introduced in the later stages to enhance exploitation. Additionally, a color correction technique is applied to correct color deviations in underwater images, leading to a more natural appearance. To verify the performance of the method, it is compared with different methods. As demonstrated by simulations, the proposed method outperforms existing algorithms in multiple underwater image enhancement metrics.