Understanding and Evaluating Blind Deconvolution Algorithms
Anat Levin, Yair Weiss, Fredo Durand and William T. Freeman
Conference on Computer Vision and Pattern Recognition (CVPR)
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.