  This paper introduces the physics-inspired synthesized underwater image
dataset (PHISWID), a dataset tailored for enhancing underwater image processing
through physics-inspired image synthesis. Deep learning approaches to
underwater image enhancement typically demand extensive datasets, yet acquiring
paired clean and degraded underwater ones poses significant challenges. While
several underwater image datasets have been proposed using physics-based
synthesis, a publicly accessible collection has been lacking. Additionally,
most underwater image synthesis approaches do not intend to reproduce
atmospheric scenes, resulting in incomplete enhancement. PHISWID addresses this
gap by offering a set of paired ground-truth (atmospheric) and synthetically
degraded underwater images, showcasing not only color degradation but also the
often-neglected effects of marine snow, a composite of organic matter and sand
particles that considerably impairs underwater image clarity. The dataset
applies these degradations to atmospheric RGB-D images, enhancing the dataset's
realism and applicability. PHISWID is particularly valuable for training deep
neural networks in a supervised learning setting and for objectively assessing
image quality in benchmark analyses. Our results reveal that even a basic U-Net
architecture, when trained with PHISWID, substantially outperforms existing
methods in underwater image enhancement. We intend to release PHISWID publicly,
contributing a significant resource to the advancement of underwater imaging
technology.
