Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
ACM Computing Surveys (CSUR)
Digital Picture Processing
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Dictionary learning algorithms for sparse representation
Neural Computation
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
Learning Sparse Overcomplete Codes for Images
Journal of VLSI Signal Processing Systems
Fast non-local algorithm for image denoising
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A robust and fast non-local means algorithm for image denoising
Journal of Computer Science and Technology
Fast non local means denoising for 3d MR images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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Nonlocal means (NLM) image denoising algorithm is not feasible in many applications due to its high computational cost. High computational burden is due to the search of similar patches for each reference patch in the entire image. In this paper, we present a novel technique of preselecting and grouping the similar patches in the form of a dictionary and hence speeding up the computation of NLM denoising method. We build a dictionary only once, with a set of training images of all possible classes of objects, in which patches with similar photometric structures are clustered together. For each noisy patch, similar patches are searched in the global dictionary. In contrast with previous NLM speedup strategies, our dictionary building approach preclassifies similar patches with the same distance measure as used by NLM method. We achieve a substantial reduction in computational time than the original NLM method especially when search window of NLM is large, without much affecting the PSNR. The proposed algorithm is shown to outperform other prefiltering based fast NLM algorithms computationally as well as qualitatively.