A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biological Cybernetics
Shape From Texture: Integrating Texture-Element Extraction and Surface Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Investigation of Methods for Determining Depth from Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth from defocus: a spatial domain approach
International Journal of Computer Vision
Rational Filters for Passive Depth from Defocus
International Journal of Computer Vision
Digital Image Restoration
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blind image restoration by anisotropic regularization
IEEE Transactions on Image Processing
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
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In this paper, we propose a method to restore a single image affected by space-varying blur. The main novelty of our method is the use of recurring patterns as regularization during the restoration process. We postulate that restored patterns in the deblurred image should resemble other sharp details in the input image. To this purpose, we establish the correspondence of regions that are similar up to Gaussian blur. When two regions are in correspondence, one can perform deblurring by using the sharpest of the two as a proposal. Our solution consists of two steps: First, estimate correspondence of similar patches and their relative amount of blurring; second, restore the input image by imposing the similarity of such recurring patterns as a prior. Our approach has been successfully tested on both real and synthetic data.