Multi-weight and multi-granularity fusion of underwater image enhancement

Light is scattered and absorbed as it travels through the water, which results in color shift, poor contrast, uneven illumination, and blurred details. This is not conducive to the exploration of marine life, the protection of marine ecology, and the development of marine engineering. For the problems of visual unnaturalness and blurred details in the enhanced underwater images, we propose a multi-weight and multi-granularity underwater image enhancement algorithm. The algorithm is built on the fusion of two images, which are derived from color-corrected and contrast-adjusted versions of an original degraded image. Based on this, their associated weight maps, i.e., Laplace contrast weight, local contrast weight, saliency weight, exposure weight, and saturation weight, are normalized and then fused with multi-granularities to solve the problem of the visual unnaturalness of images due to uneven illumination. Further, we use double scale decomposition to obtain two high-frequency components and then fuse them into image fusion to enhance image contrast and highlight image details. We test the subjective results and objective evaluations of the proposed algorithm on several datasets. The subjective results demonstrate our algorithm not only improves contrast, color naturalness, and brightness but also enhances the details of underwater images. The objective evaluation shows that the average values of UCIQE and PCQI of our algorithm outperform the other six different classical algorithms.