Boths: Super Lightweight Network-Enabled Underwater Image Enhancement

作者：Liu, Xu; Lin, Sen^*; Chi, Kaichen; Tao, Zhiyong; Zhao, Yang

来源：IEEE Geoscience and Remote Sensing Letters, 2023, 20: 1500405.

DOI：10.1109/LGRS.2022.3230049

• 3-D attention learning
• high- and low-frequency loss functions
• structure and detail interaction
• underwater image enhancement

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 Best of Both Worlds (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: https://github.com/perseveranceLX/Boths.

• 单位
  西北工业大学