Pseudo-Retinex decomposition-based unsupervised underwater image enhancement and beyond

Underwater images suffer from color casts and low contrast degraded due to wavelength-dependent light scatter and abortion of the underwater environment. To effectively improve the quality of the underwater images, deep learning-based underwater image enhancement methods have been widely proposed. However, most deep learning-based underwater image enhancement methods rely heavily on paired datasets. Actually, obtaining distortion-free images as reference images is difficult in underwater imaging. To address this problem, a fully Unsupervised convolution neural network-based Underwater Image Enhancement (UUIE) is proposed by pseudo-Retinex decomposition. The innovation of the proposed UUIE is to establish a relationship between the underwater imaging model and the Retinex model, then use terrestrial images to replace underwater images for training and estimate pseudo-illumination and pseudo-reflection maps through self-supervision using the pseudo-Retinex decomposition. The pseudo-reflection image and pseudo-illumination image are reconstructed by the pseudo-Retinex decomposition to obtain the enhanced image. Additionally, the proposed UUIE can also be extended to image dehazing and low-light enhancement with only one trained model. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of the proposed UUIE quantitatively and qualitatively.