Neuromorphic Computing Network for Underwater Image Enhancement and Beyond
Fengqi Xiao, Jiahui Liu, Yifan Huang, En Cheng, Fei Yuan · IEEE Transactions on Geoscience and Remote Sensing · 2024
Optical remote sensing serves as a critical technology for exploring underwater environments. However, light absorption and scattering underwater significantly degrade underwater optical images, affecting the extraction and analysis of information. Underwater image enhancement (UIE) methods aim to eliminate this degradation and improve the visual quality of images. Nonetheless, the complex and dynamic underwater imaging environment, limited computing resources, and scarce training data/data pairs restrict the practical application of existing methods. To solve these problems, we propose an UIE network (UIEN) based on neuromorphic computing, which simulates the pathway of the visual system to perceive and process light information, and can use a lightweight network structure to achieve good performance through unsupervised learning. Specifically, we propose a visual perception module comprising a 2-D Duffing oscillator (2D-DO) with pixel-wise potential barrier parameters. This module can generate the stochastic resonance (SR) phenomenon to enhance the degraded image. Inspired by physics-informed learning, a dual-path neural network is employed to estimate the potential barrier parameters and solve the partial differential equation (PDE) that describes the visual perception module. Subsequently, we introduce three nonreference (NR) losses to guide the network training and improve the enhanced image’s visual quality. Extensive experiments demonstrate that the proposed method can achieve outstanding performance with less computing resource cost compared to state-of-the-art (SOTA) methods. Furthermore, we examine the generalization and versatility of the proposed method to establish its reliability across various degradation types and tasks in practical applications of optical remote sensing.