Abstract

We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2-D image fragments (e.g., blocks) into 3-D data arrays which we call "groups." Collaborative filtering is a special procedure developed to deal with these 3-D groups. We realize it using the three successive steps: 3-D transformation of a group, shrinkage of the transform spectrum, and inverse 3-D transformation. The result is a 3-D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and, at the same time, it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions. Because these blocks are overlapping, for each pixel, we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail; an extension to color-image denoising is also developed. The experimental results demonstrate that this computationally scalable algorithm achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.

Keywords

Noise reductionNon-local meansSparse approximationComputer scienceArtificial intelligenceVideo denoisingBlock (permutation group theory)Wiener filterRedundancy (engineering)PixelTransformation (genetics)AlgorithmPattern recognition (psychology)MathematicsImage denoisingVideo processing

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Publication Info

Year
2007
Type
article
Volume
16
Issue
8
Pages
2080-2095
Citations
8808
Access
Closed

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Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik et al. (2007). Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering. IEEE Transactions on Image Processing , 16 (8) , 2080-2095. https://doi.org/10.1109/tip.2007.901238

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DOI
10.1109/tip.2007.901238