Underwater Image Enhancement based on Retinex Decomposition and Unsupervised Generative Adversarial Networks - Lai, Yong; Zhang, Xuebo; He, Zhouyan; Song, Yang; Luo, Ting; Xu, Haiyong (Recent Patents on Engineering, 2025)
BackgroundDue to the difficulty of obtaining the real dataset of paired underwater images, it is urgent to build an unsupervised underwater image enhancement network.ObjectiveTo address the problem, a novel underwater image enhancement based on Retinex decomposition and Unsupervised Generative Adversarial Network (RUGAN) is proposed.MethodsA color correction module is proposed considering the different color distortions of underwater images. Further, considering the human visual perception mechanism, the RUGAN network, which is similar to U-Net, is constructed using the characteristics of underwater imaging and Retinex decomposition. Based on Retinex decomposition and the characteristics of underwater imaging, the RUGAN network similar to U-Net is constructed. The reflectance image and illumination image are obtained. The reflectance image with a better effect is taken as the enhancement result. Unlike the previous supervised methods, RUGAN adopts clear air images and distorted underwater images as training. RUGAN adopts the underwater image of the color correction module as pseudo-ground truth to achieve an unsupervised effect.ResultsThe superiority of RUGAN network is further supported by extensive experiments that compared it with more methods.ConclusionThe RUGAN performs well both subjectively and objectively.
Keywords: reflectance image