Underwater image enhancement via a channel‐wise transmission estimation network - Qiang Wang; Bo Fu; Huijie Fan (IET Image Processing, 2023)
Abstract Underwater image enhancement for image processing and underwater robotic vision have recently attracted much academic attention. However, in most existing methods, underwater image enhancement is completed with a simple assumption: the attenuation coefficients are unified across the color channels. This assumption leads to unstable and visually unpleasing enhancement results. Moreover, these methods cannot be successfully applied to explore relatively independent transmissions from multiple color channels with complimentary feature information. To address these challenges, a novel channel‐wise transmission estimation network (CTEN) is proposed, which aims to pioneer the exploration of the transmission difference across the color channels in an underwater scene. Specifically, a color‐specific correction module is proposed to automatically quantify the transmission ability of multiple color channels in the underwater environment. Furthermore, a channel‐wise transmission estimation module is designed to simultaneously explore the relative independence of multi‐color channels and estimate the medium transmissions for each color channel, which represents the attenuation degree of different color radiances after reflecting in the water. Then, a novel residual strategy is introduced to integrate these two modules to complete the underwater enhancement. Using the model, the authors are able to provide an answer as to why channel‐wise transmission estimation are better than single transmission estimation and establish a generalization theory to show the effect of the independent transmission estimation model for each color channel. Experiments on several underwater image datasets verify the superiority of the proposed CTEN model.