PhISH-Net: Physics Inspired System for High Resolution Underwater Image Enhancement

Aditya Chandrasekar, Manogna Sreenivas, Soma Biswas; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 1506-1516

Underwater imaging presents numerous challenges due to refraction, light absorption, and scattering, resulting in color degradation, low contrast, and blurriness. Enhancing underwater images is crucial for high-level computer vision tasks, but existing methods either neglect the physics-based image formation process or require expensive computations. In this paper, we propose an effective framework that combines a physics-based Underwater Image Formation Model (UIFM) with a deep image enhancement approach based on the retinex model. Firstly, we remove backscatter by estimating attenuation coefficients using depth information. Then, we employ a retinex model-based deep image enhancement module to enhance the images. To ensure adherence to the UIFM, we introduce a novel Wideband Attenuation prior. The proposed PhISH-Net framework achieves real-time processing of high-resolution underwater images using a lightweight neural network and a bilateral-grid-based upsampler. Extensive experiments on two underwater image datasets demonstrate the superior performance of our method compared to state-of-the-art techniques. Additionally, qualitative evaluation on a cross-dataset scenario confirms its generalization capability. Our contributions lie in combining the physics-based UIFM with deep image enhancement methods, introducing the wideband attenuation prior, and achieving superior performance and efficiency.