RGB/Event signal fusion framework for multi-degraded underwater image enhancement

Abstract

Underwater images often suffer from various degradations, such as color distortion, reduced visibility, and uneven illumination, caused by light absorption, scattering, and artificial lighting. However, most existing methods have focused on addressing singular or dual degradation aspects, lacking a comprehensive solution to underwater image degradation. This limitation hinders the application of vision technology in underwater scenarios. In this paper, we propose a framework for enhancing the quality of multi-degraded underwater images. This framework is distinctive in its ability to concurrently address color degradation, hazy blur, and non-uniform illumination by fusing RGB and Event signals. Specifically, an adaptive underwater color compensation algorithm is first proposed, informed by an analysis of the color degradation characteristics prevalent in underwater images. This compensation algorithm is subsequently integrated with a white balance algorithm to achieve color correction. Then, a dehazing method is developed, leveraging the fusion of sharpened images and gamma-corrected images to restore blurry details in RGB images and event reconstruction images. Finally, an illumination map is extracted from the RGB image, and a multi-scale fusion strategy is employed to merge the illumination map with the event reconstruction image, effectively enhancing the details in dark and bright areas. The proposed method successfully restores color fidelity, enhances image contrast and sharpness, and simultaneously preserves details of the original scene. Extensive experiments on the public dataset DAVIS-NUIUIED and our dataset DAVIS-MDUIED demonstrate that the proposed method outperforms state-of-the-art methods in enhancing multi-degraded underwater images.