Visual-Quality-Driven Learning for Underwater Vision Enhancement

TL;DR

This paper introduces a CNN-based underwater image enhancement method guided solely by image quality metrics, effectively improving visual quality without needing ground truth data.

Contribution

It presents a novel ground-truth-free learning approach for underwater image enhancement using quality metrics as guidance.

Findings

Improved visual quality of underwater images.

Preservation of image edges during enhancement.

Good performance on the UCIQE metric.

Abstract

The image processing community has witnessed remarkable advances in enhancing and restoring images. Nevertheless, restoring the visual quality of underwater images remains a great challenge. End-to-end frameworks might fail to enhance the visual quality of underwater images since in several scenarios it is not feasible to provide the ground truth of the scene radiance. In this work, we propose a CNN-based approach that does not require ground truth data since it uses a set of image quality metrics to guide the restoration learning process. The experiments showed that our method improved the visual quality of underwater images preserving their edges and also performed well considering the UCIQE metric.