Abstract
A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. The method proposed in this paper is a fully automatic 3D blockwise version of the nonlocal (NL) means filter with wavelet subbands mixing. The proposed wavelet subbands mixing is based on a multiresolution approach for improving the quality of image denoising filter. Quantitative validation was carried out on synthetic datasets generated with the BrainWeb simulator. The results show that our NL-means filter with wavelet subbands mixing outperforms the classical implementation of the NL-means filter in terms of denoising quality and computation time. Comparison with wellestablished methods, such as nonlinear diffusion filter and total variation minimization, shows that the proposed NL-means filter produces better denoising results. Finally, qualitative results on real data are presented.
Highlights
Image denoising can be considered as a component of processing or as a process itself.In the first case, the image denoising is used to improve the accuracy of various image processing algorithms such as registration or segmentation
The results show that our NL-means filter with wavelet subbands mixing outperforms the classical implementation of the NL-means filter in terms of denoising quality and computation time
This paper presented a fully automated blockwise version of the nonlocal means filter with subbands wavelet mixing
Summary
Image denoising can be considered as a component of processing or as a process itself.In the first case, the image denoising is used to improve the accuracy of various image processing algorithms such as registration or segmentation. In the second case,the noise removal aims at improving the image quality for visual inspection. To improve this filter with an automatic tuning of the filtering parameter, a blockwise implementation and a mixing of wavelet su-bands based on the approach proposed in [2]. These contributions lead to a fully-automated method and overcome the main limitation of the classical NL-means: the computational burden.
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