Boths: Super Lightweight Network-Enabled Underwater Image Enhancement
Xu Liu, Sen Lin, Kaichen Chi, Zhiyong Tao, Yang Zhao
Abstract
Since light is scattered and absorbed by water, underwater images have inherent degradation (e.g., hazing, color shift), consequently impeding the development of remotely operated vehicles (ROVs). Toward this end, we propose a novel method, referred to as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${B}$ </tex-math></inline-formula> est <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${o}\text{f}$ </tex-math></inline-formula> Bo th World <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${s}$ </tex-math></inline-formula> (Boths). With parameters of only 0.0064 M, Boths can be considered a super lightweight neural network for underwater image enhancement. On the whole, it has three levels: structure and detail features; pixel and channel dimensions; high- and low-frequency information. Each of these three levels represents “Best of Both Worlds.” Initially, by interacting with structure and detail features, Boths can focus on these two aspects at the same time. Further, our network can simultaneously consider channel and pixel dimensions through 3-D attention learning, which is more similar to human visual perception. Lastly, the proposed model can focus on high- and low-frequency information, through a novel loss function based on the wavelet transforms. Upon subsequent analysis and evaluation, Boths has shown superior performance compared with state-of-the-art (SOTA) methods. Our models and datasets are publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/perseveranceLX/Boths</uri> .