Multi-domain conditional prior network for water-related optical image enhancement

Abstract

Water-related optical image enhancement improves the perception of information for human and machine vision, facilitating the development and utilization of marine resources. Due to the absorption and scattering of light in different water media, water-related optical images typically suffer from color distortion and low contrast. However, existing enhancement methods struggle to accurately simulate the imaging process in real underwater environments. To model and invert the degradation process of water-related optical images, we propose a Multi-domain Conditional Prior Network (MCPN) based on color vector prior and spectrum vector prior for enhancing water-related optical images. MCPN captures color, luminance, and structural priors across different feature spaces, resulting in a lightweight architecture that enhances water-related optical images while preserving critical information fidelity. Specifically, MCPN includes a modulated network, and a conditional network comprises two conditional units. The modulated network is a lightweight Convolutional Neural Network responsible for image reconstruction and local feature refinement. To avoid feature loss from multiple extractions, the Gaussian Conditional Unit (GCU) extracts atmospheric light and color shift information from the input image to form color prior vectors. Simultaneously, incorporating the Fast Fourier Transform, the Spectrum Conditional Unit (SCU) extracts scene brightness and structure to form spectrum prior vectors. These prior vectors are embedded into the modulated network to guide the image reconstruction. MCPN utilizes a PAL-based weighted Selective Supervision (PSS) strategy, selectively adjusting learning weights for images with excessive artificial noise. Experimental results demonstrate that MCPN outperforms existing methods, achieving excellent performance on the UIEB dataset. The PSS also shows fine feature matching in downstream applications.