The dim shooting environment and light scattering and absorption frequently result in degraded underwater images. The images are characterized by uneven brightness, low contrast, color deterioration, and blurred details. Existing underwater image enhancement methods excel in full-reference and non-reference metrics, yet may fail to align with human visual tendencies. To make the restored images more consistent with natural visual effect, an underwater image enhancement framework named BRIUIE is proposed. BRIUIE draws inspiration from the morphology and functions of various cell layers in the vertebrate retina. Following the visual transmission mechanisms of retinal signals, image brightness is balanced by simulating the feedback and dynamic regulation processes of horizontal cells in response to illumination variation. Meanwhile, simulating the center-surround receptive fields of bipolar and ganglion cells and implementing the color opponent mechanism effectively mitigate color distortion and low contrast. The designed multi-scale feature fusion module facilitates the complementary advantage of the ON and OFF visual pathways of ganglion cells, employing a contrastive learning strategy to prevent overfitting because of simple consistency loss. Comprehensive full-/non-reference experiments demonstrate the proposed BRIUIE outperforms other SOTA methods in quantitative evaluations, while also delivering qualitative results that closely align with human visual assessment standards.

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.