Performance evaluation of wavelet, ridgelet, curvelet and contourlet transforms based techniques for digital image denoising-Reference-Cited by-同舟云学术

Performance evaluation of wavelet, ridgelet, curvelet and contourlet transforms based techniques for digital image denoising

Published:2015-12-16 Issue:4 Volume:45 Page:509-533
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Kamble Vipin Milind^ORCID,Parlewar Pallavi,Keskar Avinash G.,Bhurchandi Kishor M.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

http://link.springer.com/content/pdf/10.1007/s10462-015-9453-7.pdf

Reference77 articles.

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2. Bala E, Ertüzün A (2005) A multivariate thresholding technique for image denoising using multiwavelets EURASIP. J Appl Signal Process 2005:1205–1211

3. Bo C, Zexun G, Yang Y, Tianshuang S (2007) Dual-tree Complex Wavelets Transforms for Image Denoising. In: Eighth ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing, Qingdao, China, IEEE, pp 70–74

4. Bose T (2004) Digital signal and image processing. Wiley, London

5. Buades A, Coll B, Morel JM (2004) On image denoising methods. Technical Note, CMLA (Centre de Mathematiques et de Leurs Applications)

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