Lightweight method predicts corrections to enhance underwater images
PIC-UIE: Predicting Image-Adaptive Corrections for Lightweight Underwater Image Enhancement
Computer Vision and Pattern Recognition
Summary
Underwater photos often look blurry and have weird colors because water changes how light works. The authors created a simple system that looks at a small version of a photo to guess how to fix colors and brightness, then applies those fixes to the full photo. This method needs fewer calculations than older ways and works well even on big photos quickly. By working in a special color space and using a few smart steps, their system improves underwater pictures without heavy computing.
What this means in practice
- •For camera hardware engineers: Integrate the lightweight correction predictor to improve underwater camera image quality without extra processing power or latency.
- •For marine robotics teams: Enhance real-time underwater robot vision by applying adaptive color and detail corrections efficiently at high resolution.
Authors
Cunhao Zhu, Dongliang Xu, Xiangtao Kong, Xiaoyan Lu, Tianyu Wang, Yue Yao
Abstract
Underwater image enhancement (UIE) aims to restore visibility, color fidelity, and structural detail from images degraded by wavelength-dependent attenuation and backscatter. State-of-the-art UIE methods often rely on large backbones and dense image-to-image prediction, limiting their practicality for edge deployment. Moreover, operating entirely in a single color space couples degradation estimation with luminance and chroma correction. To address these challenges, we propose PIC-UIE, a lightweight predictor--executor framework that predicts image-adaptive corrections from a fixed $256\times256$ RGB thumbnail and applies them to the native-resolution input in the YCbCr color space. The predictor produces seven outputs, organized into spatial correction, nonlinear luminance and coupled chroma mapping, and image-level color calibration. A depth map regularizes the transmission proxy during training, whereas inference uses only the RGB input. With 9,486 parameters and 0.094 GFLOPs at $256\times256$, PIC-UIE achieves 24.137 dB PSNR and 0.9216 SSIM on UIEB-90 and 21.320 dB PSNR on zero-shot LSUI. It further processes native 4K images at 55.0 FPS under the comparison protocol. These results show that structured correction prediction provides an effective and practical alternative to dense RGB reconstruction for underwater image enhancement.