Understanding and evaluating blind deconvolution algorithms
Author(s)Durand, Fredo; Levin, Anat; Weiss, Yair; Freeman, William T.
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Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. Recent algorithms have afforded dramatic progress, yet many aspects of the problem remain challenging and hard to understand. The goal of this paper is to analyze and evaluate recent blind deconvolution algorithms both theoretically and experimentally. We explain the previously reported failure of the naive MAP approach by demonstrating that it mostly favors no-blur explanations. On the other hand we show that since the kernel size is often smaller than the image size a MAP estimation of the kernel alone can be well constrained and accurately recover the true blur. The plethora of recent deconvolution techniques makes an experimental evaluation on ground-truth data important. We have collected blur data with ground truth and compared recent algorithms under equal settings. Additionally, our data demonstrates that the shift-invariant blur assumption made by most algorithms is often violated.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009
Institute of Electrical and Electronics Engineers
Levin, A. et al. “Understanding and evaluating blind deconvolution algorithms.” Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. 2009. 1964-1971. ©2009 Institute of Electrical and Electronics Engineers.
Final published version
INSPEC Accession Number: 10836014