Underwater image restoration via feature priors to estimate background light and optimized transmission map

Underwater images frequently suffer from color casts and poor contrast, due to the absorption and scattering of light in water medium. To address these two degradation issues, we propose an underwater image restoration method based on feature priors inspired by underwater scene prior. Concretely, we first develop a robust model to estimate the background light according to feature priors of flatness, hue, and brightness, which can effectively relieve color distortion. Next, we compensate the red channel of color corrected image to revise the transmission map of it. Coupled with the structure-guided filter, the coarse transmission map is refined. The refined transmission map preserves the edge information while improving the contrast. Extensive experiments on diverse degradation scenes demonstrate that our method achieves superior performance against several state-of-the-art methods. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

This paper presents an underwater image restoration method based on feature priors, which aims to address color distortion and poor contrast issues in underwater images. By estimating background light and refining the transmission map of color corrected images, the method effectively improves the overall image quality, achieving superior performance compared to state-of-the-art methods in diverse degradation scenarios.