Benchmarking Underwater Image Enhancement and Restoration, and Beyond
Guojia Hou, Xin Zhao, Zhenkuan Pan, Huan Yang, Lu Tan, Jingming Li
Abstract
Image enhancement and restoration is among the most investigated topics in the field of underwater machine vision. The objective image quality assessment is a fundamental part of optimizing underwater enhancement and restoration technologies. However, most no-reference (NR) metrics are not specifically designed for underwater image quality assessment. Moreover, since the reference (undegraded) images are not available in underwater scenes, the classical full-reference (FR) metrics cannot be used to evaluate underwater image enhancement and restoration methods. In this paper, we first design an underwater image synthesis algorithm (UISA), in which depending on the real-world underwater image, we can produce a synthetic underwater image from an outdoor ground-truth image. Based on this strategy, we establish a new large-scale benchmark that contains ground-truth images and synthetic underwater images of the same scene, called synthetic underwater image dataset (SUID). Our SUID is constructed on the basis of the underwater image formation model (IFM) and characteristics of underwater optical propagation, possessing solid reliability and feasibility. The proposed SUID creates possibility for a FR evaluation of existing technologies for underwater image enhancement and restoration, which is illustrated by performing extensive experiments and quantitative analysis. The SUID is available online at: http://dx.doi.org/10.21227/agdr-y109.