Most image restoration techniques build "universal" image priors, trained on a variety of scenes, which can guide the restoration of any image. But what if we have more specific training examples, e.g. sharp images of similar scenes? Surprisingly, state-of-the-art image priors don't seem to benefit from from context-specific training examples. Re-training generic image priors using ideal sharp example images provides minimal improvement in non-blind deconvolution. To help understand this phenomenon we explore non-blind deblurring performance over a broad spectrum of training image scenarios. We discover two strategies that become beneficial as example images become more context-appropriate: (1) locally adapted priors trained from region level correspondence significantly outperform globally trained priors, and (2) a novel multi-scale patch-pyramid formulation is more successful at transferring mid and high frequency details from example scenes. Combining these two key strategies we can qualitatively and quantitatively outperform leading generic non-blind deconvolution methods when context-appropriate example images are available. We also compare to recent work which, like ours, tries to make use of context-specific examples. © 2014 Springer International Publishing.
CITATION STYLE
Sun, L., Cho, S., Wang, J., & Hays, J. (2014). Good image priors for non-blind deconvolution: Generic vs. specific. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8692 LNCS, pp. 231–246). Springer Verlag. https://doi.org/10.1007/978-3-319-10593-2_16
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