Pairwise-Comparison-Based Rank Learning for Benchmarking Image Restoration Algorithms

Image restoration has attracted substantial attention recently and many image restoration algorithms have been proposed for restoring latent clear images from degraded images. However, determining how to objectively evaluate the performances of these algorithms remains an open problem, which may hin...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2019-08, Vol.21 (8), p.2042-2056
Main Authors: Hu, Bo, Li, Leida, Liu, Hantao, Lin, Weisi, Qian, Jiansheng
Format: Article
Language:English
Subjects:
Algorithms
Benchmark testing
Benchmarks
Distortion
Feature extraction
Image quality
Image restoration
image structure
Machine learning
Measurement
Pairwise comparison
Prediction algorithms
Predictive models
Quality
rank learning
Ranking
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Summary:Image restoration has attracted substantial attention recently and many image restoration algorithms have been proposed for restoring latent clear images from degraded images. However, determining how to objectively evaluate the performances of these algorithms remains an open problem, which may hinder the further development of advanced image restoration techniques. Most image restoration-quality metrics are designed for specific restoration applications; hence, their generalization ability is limited. For benchmarking image restoration algorithms, the ranking of restored images that are generated via various algorithms, is the most heavily considered factor. Inspired by this, this paper presents a pairwise-comparison-based rank learning framework for benchmarking the performances of image restoration algorithms, which focuses on the relative quality ranking of restored images. Under the proposed framework, we further propose a general image restoration quality metric by integrating quality-aware features in both the spatial and frequency domains. The proposed metric exhibits good generalization performance, and it is applicable to various restoration applications. The results of extensive experiments that were conducted on eight public databases of five restoration scenarios demonstrate the superior performance of the proposed method over the existing quality metrics. Moreover, the proposed framework is used to improve the existing quality metrics for benchmarking image restoration algorithms and highly encouraging results are obtained.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2019.2894958