GPLM: Enhancing underwater images with Global Pyramid Linear Modulation

Abstract

Underwater imagery often suffers from challenges such as color distortion, low contrast, blurring, and noise due to the absorption and scattering of light in water. These degradations complicate visual interpretation and hinder subsequent image processing. Existing methods struggle to effectively address the complex, spatially varying degradations without prior environmental knowledge or may produce unnatural enhancements. To overcome these limitations, we propose a novel method called Global Pyramid Linear Modulation that integrates physical degradation modeling with deep learning for underwater image enhancement. Our approach extends Feature-wise Linear Modulation to a four-dimensional structure, enabling fine-grained, spatially adaptive modulation of feature maps. Our method captures multi-scale contextual information by incorporating a feature pyramid architecture with self-attention and feature fusion mechanisms, effectively modeling complex degradations. We validate our method by integrating it into the MixDehazeNet model and conducting experiments on benchmark datasets. Our approach significantly improves the Peak Signal-to-Noise Ratio, increasing from 28.6 dB to 30.6 dB on the EUVP-515-test dataset. Compared to recent state-of-the-art methods, our method consistently outperforms them by over 3 dB in PSNR on datasets with ground truth. It improves the Underwater Image Quality Measure by more than one on datasets without ground truth. Furthermore, we demonstrate the practical applicability of our method on a real-world underwater dataset, achieving substantial improvements in image quality metrics and visually compelling results. These experiments confirm that our method effectively addresses the limitations of existing techniques by adaptively modeling complex underwater degradations, highlighting its potential for underwater image enhancement tasks.