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.
Highlights
With the wide underwater applications including underwater geological exploration, underwater biological exploration, object recognition, artificial intelligence and other related activities, underwater image processing has attracted more attentions in recent years
In this paper, we construct a large-scale synthetic underwater image dataset containing 900 images with different degraded types and turbid levels based on the proposed underwater image synthesis algorithm
A synthetic underwater image is generated by assigning the values of the acquired background light (BL) and transmission map (TM) into the underwater image formation model (IFM)
Summary
With the wide underwater applications including underwater geological exploration, underwater biological exploration, object recognition, artificial intelligence and other related activities, underwater image processing has attracted more attentions in recent years. Since the reference (undegraded) images are not available in underwater scenario, the no-reference image quality assessment (NR-IQA) strategies [45]–[53] are commonly adopted without requiring any reference images These methods can be divided into two categories depending on whether considering the prior knowledge of the distortion type: distortion-specific (DS) [45]–[49] and non-distortion-specific (NDS) [50]–[53]. The existing classical full-reference (FR) evaluation are not available for evaluating these schemes on account of lacking underwater reference image This limitation would hinder the progress of underwater image enhancement and restoration technologies, VOLUME 8, 2020 and quality evaluation. (iii) The constructed large-scale SUID contains 900 degraded images with different turbidity types and degradation levels by reconstructing four common underwater challenge scenes including greenish scene, bluish scene, low-light scene, hazy scene.
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