UIF: An Objective Quality Assessment for Underwater Image Enhancement
Yannan Zheng, Weiling Chen, Rongfu Lin, Tiesong Zhao

TL;DR
This paper introduces UIF, an objective quality assessment metric for underwater image enhancement that combines statistical features related to naturalness, sharpness, and structure, validated on a new large-scale database.
Contribution
The paper proposes a novel UIF metric that effectively evaluates underwater image enhancement quality using statistical features and support vector regression, filling a gap in existing methods.
Findings
UIF outperforms existing quality metrics in experiments
A large-scale UIE database (UIED) is established for benchmarking
The method effectively assesses visual improvements in underwater images
Abstract
Due to complex and volatile lighting environment, underwater imaging can be readily impaired by light scattering, warping, and noises. To improve the visual quality, Underwater Image Enhancement (UIE) techniques have been widely studied. Recent efforts have also been contributed to evaluate and compare the UIE performances with subjective and objective methods. However, the subjective evaluation is time-consuming and uneconomic for all images, while existing objective methods have limited capabilities for the newly-developed UIE approaches based on deep learning. To fill this gap, we propose an Underwater Image Fidelity (UIF) metric for objective evaluation of enhanced underwater images. By exploiting the statistical features of these images, we present to extract naturalness-related, sharpness-related, and structure-related features. Among them, the naturalness-related and…
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