Paper Info
Title
Accelerating non-local denoising with a patch based dictionary
Abstract
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.
Year
DOI
Venue
2012
10.1145/2425333.2425349
ICVGIP
Keywords
Field
DocType
non-local denoising,similar patch,dictionary building approach,original nlm method,computational time,nlm denoising method,similar photometric structure,nlm algorithm,previous nlm speedup strategy,global dictionary,nlm method,non local means,clustering
Noise reduction,Computer vision,Pattern recognition,Non-local means,Computer science,Artificial intelligence,Image denoising,Cluster analysis,Speedup,Computation
Conference
Citations 
PageRank 
References 
1
0.40
15
Authors
2
Name
Order
Citations
PageRank
Hemalata Bhujle1211.79
Subhasis Chaudhuri21384133.18