Feature-oriented image enhancement using shock filters
SIAM Journal on Numerical Analysis
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Image deblurring with blurred/noisy image pairs
ACM SIGGRAPH 2007 papers
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
Motion deblurring using hybrid imaging
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Ringing and noise amplification are the most dominant artifacts in image deconvolution. These artifacts can be reduced by introducing image prior into the deconvolution process. A regularization weighting factor can control the strength of regularization. Ringing and noise can be reduced significantly with the strong weighting factor, but details can be lost. We propose a nonblind image deconvolution method with adaptive regularization that can reduce ringing and noise in the smooth region and preserve image details in the textured region simultaneously. For adaptive regularization, we make a reference image that gives proper edge information and helps to restore a latent image. The reference image guides the strength of the weighting factor on the pixel of the blurred image. Experimental results show that ringing and noise are suppressed efficiently, while preserving image details.