Visual-Quality-Driven Learning for Underwater Vision Enhancement
This addresses the problem of underwater image restoration for applications like marine research, but it is incremental as it builds on existing CNN and quality metric approaches.
The paper tackled the challenge of enhancing underwater images without ground truth data by proposing a CNN-based method guided by image quality metrics, which improved visual quality and performed well on the UCIQE metric.
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.