Joint Iterative Color Correction and Dehazing for Underwater Image Enhancement

The captured underwater images suffer from color cast and haze effect caused by absorption and scattering. These interdependent phenomena jointly degrade images, resulting in failure of autonomous machines to recognize image contents. Most existing learning-based methods for underwater image enhancement (UIE) treat the degraded process as a whole and ignore the interaction between color correction and dehazing. Thus, they often obtain unnatural results. To this end, we propose a novel joint network to optimize the results of color correction and dehazing in multiple iterations. Firstly, a novel triplet-based color correction module is proposed to obtain color-balanced images with identical distribution of color channels. By means of inherent constraints of the triplet structure, the information of channel with less distortion is utilized to recover the information of other channels. Secondly, a recurrent dehazing module is designed to alleviate haze effect in images, where the Gated Recurrent Unit (GRU) as the memory module optimizes the results in multiple cycles to deal with severe underwater distortions. Finally, an iterative mechanism is proposed to jointly optimize the color correction and dehazing. By learning transform coefficients from dehazing features, color features and basic features of raw images are progressively refined, which maintains color balanced during the dehazing process and further improves clarity of images. Experimental results show that our network is superior to the existing state-of-the-art approaches for UIE and provides improved performance for underwater object detection.