Image Denoising
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An example of nonlocal denoising using an external codebook. A noisy block, enclosed by the red square in (a), does not have well matching blocks within the image itself, but more similar blocks can be found in an external codebook. Subfigures (b) and (c) show the most similar blocks found in the input image and the external codebook.
Abstract
A nonlocal minimum mean square error (MMSE) image denoising algorithm is proposed in this work. Based on the Bayesian estimation theory, we first derive that the conventional nonlocal means filter is an MMSE estimator in the special case of noise-free nonlocal neighbors. Then, we develop the nonlocal MMSE denoising filter that can minimize the mean square error (MSE) of a denoised block in more general cases of noisy nonlocal neighbors. Furthermore, the proposed algorithm searches nonlocal neighbors from an external database as well as the entire input image to improve the performance even when a noisy block may not have similar blocks within the image. Since the extended search range demands a higher computational burden, we develop a probabilistic tree-based search method to reduce the computational complexity. Simulation results show that the proposed algorithm provides significantly better denoising performance than the conventional nonlocal means filter.
Experimental Results
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Denoising results on the “Castle” image.
Publications
Chul Lee, Chulwoo Lee, and Chang-Su Kim, “MMSE nonlocal means denoising algorithm for Poisson noise removal,” in Proc. IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, Sep. 2011, pp. 2561-2564.
Chul Lee, Chulwoo Lee, and Chang-Su Kim, “An MMSE approach to nonlocal image denoising: Theory and practical implementation,” Journal of Visual Communication and Image Representation, vol. 23, no. 3, pp. 476-490, Apr. 2012.
Code
will be available soon