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Image Deblurring and Denoising using Color Priors

Neel Joshi, C. Lawrence Zitnick, Richard Szeliski, and David J. Kriegman


Image blur and noise are difficult to avoid in many situations and can often ruin a photograph. We present a novel image deconvolution algorithm that deblurs and denoises an image given a known shift-invariant blur kernel. Our algorithm uses local color statistics derived from the image as a constraint in a unified framework that can be used for deblurring, denoising, and upsampling. A pixel’s color is required to be a linear combination of the two most prevalent colors within a neighborhood of the pixel. This two-color prior has two major benefits: it is tuned to the content of the particular image and it serves to decouple edge sharpness from edge strength. Our unified algorithm for deblurring and denoising out-performs previous methods that are specialized for these individual applications. We demonstrate this with both qualitative results and extensive quantitative comparisons that show that we can out-perform previous methods by approximately 1 to 3 DB.


Publication typeInproceedings
Published inIEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009)
AddressMiami, FL
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