ULIE: Underwater Low-Light Image Enhancement Using Deep Learning

Abstract

Among the Underwater images captured in low-light conditions are usually suffer from severe quality degradation related to insufficient illumination, wavelength-dependent colour attenuation, scattering and noise amplification, while strongly affecting the applicability in underwater vision applications. Correcting these problems while preserving computational efficiency is critical in real-time systems and systems with limited resources underwater. This thesis introduces an efficient deep learning based approach for underwater low-light image enhancement to focus on image improvement in terms of illuminating effect, structural details preservation and color fidelity restoration. This thesis presents light-weight enhancement approach to ensure low computational complexity and a composite loss function is designed to help guide the reconstruction in terms of pixel-level reconstruction, structural similarity, perceptual consistency and color-correction. The proposed method is end-to-end trained and tested on the EUVP Dark dataset by using quantitative and qualitative evaluations. Experimental results show that the proposed approach is able to get significant improvement in illumination recovery and reconstruction accuracy, and the corresponding PSNR value of 28.52, SSIM value of 0.8433 and UIQM value of 2.79. Qualitative analysis also shows increased visibility, balanced color restoration and increased detail clarity in severely degraded underwater images. These results indicate that combining an efficient network design and a task-specific formulation of loss is a good solution for underwater low-light image enhancement, especially for real-time deployment scenarios.