Underwater Image Enhancement Network Based on Octave Convolution with Physical Model Constraints
WU Cheng1, LIU Xiaohua2, TANG Guijin1
1. School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003; 2. School of Digital Media and Design Arts, Nanjing University of Posts and Telecommunications, Nanjing 210023
Abstract：In underwater image enhancement tasks,deep learning struggles with the lack of physical interpretability and the spatial redundancy in feature representation. To address these issues, an underwater image enhancement network based on Octave convolution with physical model constraints（OCPMNet） is proposed. First, an octave feature extraction block（OFEB） is designed to explicitly decompose the input image into a high-frequency branch and a low-frequency branch via octave convolution. The receptive field of the low-frequency branch is expanded through downsampling to capture global degradation, and meanwhile the computational redundancy is effectively reduced. Then, a multi-scale background light module （MBLM） is constructed to extract deep low-frequency features from the bottleneck layer of the network. Three branches are utilized to extract local, neighbor and global background light information, and the information is fused to obtain an estimated map of the ambient background light. Finally, a physics-constrained dual-domain restoration module（PDRM） is introduced. Octave convolution is adopted to fuse terminal features, yielding the estimations of the direct transmission map and the backscatter transmission map. Moreover, an initial enhanced image is reconstructed under the constraints of the Sea-Thru physical model with the background light estimated by MBLM. Subsequently, residual correction is performed simultaneously in both spatial and frequency domains to compensate for the fitting bias of the physical model. Experiments on multiple datasets demonstrate the superiority of OCPMNet in terms of objective metrics and subjective image quality. Furthermore, OCPMNet shows strong real-time processing capability with engineering application value.
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2026, Vol. 39 
