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Image denoising by sparse 3D transform-domain collaborative filtering (2007)
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| Venue: | IEEE TRANS. IMAGE PROCESS |
| Citations: | 422 - 32 self |
BibTeX
@MISC{Dabov07imagedenoising,
author = {Kostadin Dabov and Alessandro Foi and Vladimir Katkovnik and Karen Egiazarian},
title = {Image denoising by sparse 3D transform-domain collaborative filtering},
year = {2007}
}
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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.
Keyphrases
transform-domain collaborative filtering image denoising efficient implementation grouped block scalable algorithm achieves peak signal-to-noise ratio novel denoising strategy inverse 3-d transformation 3-d group significant improvement 3-d transformation successive step 3-d data array novel image 3-d estimate enhanced sparse representation transform domain grouped image block transform spectrum filtered block similar 2-d image fragment collaborative filtering reveals essential unique feature subjective visual quality many different estimate full detail developed collaborative wiener filtering collaborative filtering original position particular averaging procedure color-image denoising special procedure individual block experimental result
