UIF: An Objective Quality Assessment for Underwater Image Enhancement
- URL: http://arxiv.org/abs/2205.09392v1
- Date: Thu, 19 May 2022 08:43:47 GMT
- Title: UIF: An Objective Quality Assessment for Underwater Image Enhancement
- Authors: Yannan Zheng, Weiling Chen, Rongfu Lin, Tiesong Zhao
- Abstract summary: 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.
Experimental results confirm that the proposed UIF outperforms a variety of underwater and general-purpose image quality metrics.
- Score: 17.145844358253164
- License: http://creativecommons.org/licenses/by/4.0/
- 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 sharpness-related features evaluate
visual improvement of enhanced images; the structure-related feature indicates
structural similarity between images before and after UIE. Then, we employ
support vector regression to fuse the above three features into a final UIF
metric. In addition, we have also established a large-scale UIE database with
subjective scores, namely Underwater Image Enhancement Database (UIED), which
is utilized as a benchmark to compare all objective metrics. Experimental
results confirm that the proposed UIF outperforms a variety of underwater and
general-purpose image quality metrics.
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