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

Yiming Liu, Jue Wang, Sunghyun Cho, Adam Finkelstein, Szymon Rusinkiewicz

Research output: Contribution to journalArticlepeer-review

94 Scopus citations


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.

Original languageEnglish (US)
Article number175
JournalACM Transactions on Graphics
Issue number6
StatePublished - Nov 2013

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design


  • Deblurring
  • Image quality metric
  • No-reference
  • Percetuallyvalidated


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