Retinex-based underwater image enhancement via adaptive color correction and hierarchical U-shape transformer

Abstract

Underwater images can suffer from visibility and quality degradation due to the attenuation of propagated light and other factors unique to the underwater setting. While Retinex-based approaches have shown to be effective in enhancing the underwater image quality, the use of hand-crafted priors and optimization-driven solutions often prevent the adaptivity of these methods to different types of underwater images. Moreover, the commonly-used white balance strategy which often appears in the preprocessing stage of the underwater image enhancement (UIE) algorithms may give rise to unwanted color distortions due to the fact that wavelength-dependent light absorption is not taken into account. To overcome these potential limitations, in this paper, we present an effective UIE model based on adaptive color correction and data-driven Retinex decomposition. Specifically, an adaptive color balance approach which takes into account different attenuation levels for light with different wavelengths is proposed to adaptively enhance the three color channels. Furthermore, deep neural networks are employed for the Retinex decomposition, formulating the optimization problem as an implicit-prior-regularized model which is solved by learning the priors from a large training dataset. Finally, a hierarchical U-shape Transformer network which uses hierarchically-structured multi-scale feature extraction and selective feature aggregation is applied to the decomposed images for contrast enhancement and blur reduction. Experimental results tested on six benchmark underwater image datasets demonstrate the effectiveness of the proposed UIE model.