Underwater image enhancement method via multi-feature prior fusion

Jingchun Zhou, Dehuan Zhang, Weishi Zhang

Abstract: The information in a single underwater image is insufficient due to the complexity of the underwater environment, which makes it challenging to meet the expectations of marine research. In this paper, we proposed a visual quality enhancement method for underwater images based on multi-feature prior fusion (MFPF), achieved by extracting and fusing multiple feature priors of underwater images. Complementary multi-features enhance the visual quality of underwater images. We designed a color correction method based on self-adaptive standard deviation, which realizes the color offset correction based on the dominant color of the underwater image. A gamma correction power function and spatial linear adjustment were also applied to achieve a set of artificial exposure map sequences obtained from a single degraded image and enhance the dark area’s brightness and structural details. This design makes full use of the advantages of white balance, guided filtering, and multi-exposure sequence technology. And it uses a multi-scale fusion of various prior features to enhance underwater images. The experimental results show that by applying the multi-feature prior fusion scheme, this design comprehensively solves various degenerated problems, removes over-enhancement, and improves dark details.