Abstract
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.
Abstract (translated)
本文介绍了一个基于物理图像生成的水下图像数据集（PHISWID），该数据集专门用于通过物理图像合成来增强水下图像处理。水下图像增强通常需要大量的数据，然而获取成对的水下干净和污损图像会面临重大挑战。虽然基于物理图像生成的水下图像数据集已经提出了几个，但目前还没有公开可用的集。此外，大多数水下图像合成方法并没有意图复制大气场景，导致增强效果不完整。PHISWID通过提供一组成对的水下地面（大气）和合成降解的水下图像，不仅展示了色彩降解，还突出了经常被忽视的海洋雪（由有机物质和沙子颗粒组成的复合物，对水下图像清晰度有很大影响）的影响。该数据集将这些降解应用到大气RGB-D图像中，提高了数据集的逼真度和适用性。PHISWID对于在监督学习环境中训练深度神经网络以及在基准分析中客观评估图像质量具有特别价值。我们的结果表明，即使是最基本的U-Net架构，当使用PHISWID进行训练时，也会显著优于现有方法在水下图像增强方面。我们打算将PHISWID公开发布，为水下成像技术的发展贡献重大资源。
URL
https://arxiv.org/abs/2404.03998
