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A no-reference metric for evaluating the quality of motion deblurring

Author(s): Liu, Yiming; Wang, Jue; Cho, Sunghyun; Finkelstein, Adam; Rusinkiewicz, Szymon

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Abstract: Methods to undo the effects of motion blur are the subject of intense research, but evaluating and tuning these algorithms has traditionally required either user input or the availability of ground-truth images. We instead develop a metric for automatically predicting the perceptual quality of images produced by state-of-the-art deblurring algorithms. The metric is learned based on a massive user study, incorporates features that capture common deblurring artifacts, and does not require access to the original images (i.e., is "noreference"). We show that it better matches user-supplied rankings than previous approaches to measuring quality, and that in most cases it outperforms conventional full-reference image-similarity measures. We demonstrate applications of this metric to automatic selection of optimal algorithms and parameters, and to generation of fused images that combine multiple deblurring results.
Publication Date: Nov-2013
Citation: Liu, Yiming, Jue Wang, Sunghyun Cho, Adam Finkelstein, and Szymon Rusinkiewicz. "A no-reference metric for evaluating the quality of motion deblurring." ACM Transactions on Graphics (TOG) 32, no. 6 (2013): 175:1-175:12. doi:10.1145/2508363.2508391
DOI: 10.1145/2508363.2508391
ISSN: 0730-0301
EISSN: 1557-7368
Pages: 175:1 - 175:12
Type of Material: Journal Article
Journal/Proceeding Title: ACM Transactions on Graphics
Version: Author's manuscript

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