PHISWID: physics-inspired underwater image data set synthesized from RGB-D images

This paper introduces the physics-inspired synthesized underwater image data set (PHISWID), a data set tailored for enhancing underwater image processing through physics-inspired image synthesis. For underwater image enhancement, data-driven approaches (e.g. deep neural networks) typically demand extensive data sets, yet acquiring paired clean atmospheric images and degraded underwater images poses significant challenges. Existing data sets have limited contributions to image enhancement due to a lack of physics models, publicity and ground-truth atmospheric images. PHISWID addresses these issues by offering a set of paired atmospheric and underwater images. Specifically, underwater images are synthetically degraded by color degradation, haze and marine snow artifacts from atmospheric RGB-D images. It is enabled based on a physics-based underwater image observation model. Their synthetic approach generates a large quantity of the pairs, enabling effective training of deep neural networks and objective image quality assessment. Through benchmark experiments with some data sets and image enhancement methods, they validate that their data set can improve the image enhancement performance. Their data set, which is publicly available, contributes to the development of underwater image processing.