Physics-Inspired Synthesized Underwater Image Dataset

• URL: http://arxiv.org/abs/2404.03998v2
• Date: Wed, 11 Dec 2024 10:52:47 GMT
• Title: Physics-Inspired Synthesized Underwater Image Dataset
• Authors: Reina Kaneko, Takumi Ueda, Hiroshi Higashi, Yuichi Tanaka,
• Abstract summary: PHISWID is a dataset tailored for enhancing underwater image processing through physics-inspired image synthesis. Our dataset contributes to the development in underwater image processing.
• Score: 9.117162374919715
• License:
• 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. For underwater image enhancement, data-driven approaches (e.g., deep neural networks) typically demand extensive datasets, yet acquiring paired clean and degraded underwater images poses significant challenges. Existing datasets have limited contributions to image enhancement due to lack of physics models, publicity, and ground-truth images. PHISWID addresses these issues by offering a set of paired ground-truth (atmospheric) and underwater images synthetically degraded by color degradation and marine snow artifacts. Generating underwater images from atmospheric RGB-D images based on physical models provides pairs of real-world ground-truth and degraded images. Our synthetic approach generates a large quantity of the pairs, enabling effective training of deep neural networks and objective image quality assessment. Through benchmark experiment with some datasets and image enhance methods, we validate that our dataset can improve the image enhancement performance. Our dataset, which is publicly available, contributes to the development in underwater image processing.

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