Rank algorithms for picture processing
Computer Vision, Graphics, and Image Processing
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Fast median and bilateral filtering
ACM SIGGRAPH 2006 Papers
Digital Image Processing: PIKS Scientific Inside
Digital Image Processing: PIKS Scientific Inside
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Median Filtering in Constant Time
IEEE Transactions on Image Processing
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Noise in 3D computer tomography (CT) images is close to white and becomes large when patient radiation doses are reduced. We propose two methods for noise reduction in CT images: 3D extension of fast rank algorithms (Rank-2.5D) and 3D extension of a non-local means algorithm (NLM-2.5D). We call both our algorithms "2.5D" because the extended NLM algorithm is slightly asymmetric by slice axes, while our Rank algorithms, being fully symmetric mathematically and by results, have some implementation asymmetry. A comparison of the methods is presented. It is shown that NLM-2.5D method has the best quality, but is computationally expensive: its complexity quickly rises as a function of the neighborhood size, while Rank-2.5D only shows linear growth. Another contribution of this paper is a modified multiscale histogram representation in memory with a tree-like structure. This dramatically reduces memory requirements and makes it possible to process 16-bit DICOM data with full accuracy. Artificial test sequences are used for signal-to-noise performance measurements, while real CT scans are used for visual assessment of results. We also propose two new measures for no-reference denoising quality assessment based on the autocorrelation coefficient and entropy: both measures analyze randomness of the difference between noisy and filtered images.