Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Two motion-blurred images are better than one
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Perfect blind restoration of images blurred by multiple filters: theory and efficient algorithms
IEEE Transactions on Image Processing
Blind identification of multichannel FIR blurs and perfect image restoration
IEEE Transactions on Image Processing
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Blind deconvolution of motion blur is hard, but it can be made easier if multiple images are available. This observation, and an algorithm using two differently-blurred images of a scene are the subject of this paper. While this idea is not new, existing methods have so far not delivered very practical results. In practice, the PSFs corresponding to the two given images are estimated and assumed to be close to the latent motion blurs. But in actual fact, these estimated blurs are often far from the truth, for a simple reason: They often share a common, and unidentified PSF that goes unaccounted for. That is, the estimated PSFs are themselves "blurry". While this can be due to any number of other blur sources including shallow depth of field, out of focus, lens aberrations, diffraction effects, and the like, it is also a mathematical artifact of the ill-posedness of the deconvolution problem. In this paper, instead of estimating the PSFs directly and only once from the observed images, we first generate a rough estimate of the PSFs using a robust multichannel deconvolution algorithm, and then "deconvolve the PSFs" to refine the outputs. Simulated and real data experiments show that this strategy works quite well for motion blurred images, producing state of the art results.