Feature-oriented image enhancement using shock filters
SIAM Journal on Numerical Analysis
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
ACM SIGGRAPH Asia 2009 papers
Blind deconvolution using a normalized sparsity measure
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Efficient marginal likelihood optimization in blind deconvolution
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Fast removal of non-uniform camera shake
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Non-stationary correction of optical aberrations
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A no-reference metric for evaluating the quality of motion deblurring
ACM Transactions on Graphics (TOG)
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Camera lenses are a critical component of optical imaging systems, and lens imperfections compromise image quality. While traditionally, sophisticated lens design and quality control aim at limiting optical aberrations, recent works [1,2,3] promote the correction of optical flaws by computational means. These approaches rely on elaborate measurement procedures to characterize an optical system, and perform image correction by non-blind deconvolution. In this paper, we present a method that utilizes physically plausible assumptions to estimate non-stationary lens aberrations blindly, and thus can correct images without knowledge of specifics of camera and lens. The blur estimation features a novel preconditioning step that enables fast deconvolution. We obtain results that are competitive with state-of-the-art non-blind approaches.