Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.acha.2013.10.001
Title: Data-driven tight frame construction and image denoising
Authors: Cai, J.-F.
Ji, H. 
Shen, Z. 
Ye, G.-B.
Keywords: Image de-noising
Sparse approximation
Tight frame
Wavelet thresholding
Issue Date: 2014
Citation: Cai, J.-F.,Ji, H.,Shen, Z.,Ye, G.-B. (2014). Data-driven tight frame construction and image denoising. Applied and Computational Harmonic Analysis 37 (1) : 89-105. ScholarBank@NUS Repository. https://doi.org/10.1016/j.acha.2013.10.001
Abstract: Sparsity-based regularization methods for image restoration assume that the underlying image has a good sparse approximation under a certain system. Such a system can be a basis, a frame, or a general over-complete dictionary. One widely used class of such systems in image restoration are wavelet tight frames. There have been enduring efforts on seeking wavelet tight frames under which a certain class of functions or images can have a good sparse approximation. However, the structure of images varies greatly in practice and a system working well for one type of images may not work for another. This paper presents a method that derives a discrete tight frame system from the input image itself to provide a better sparse approximation to the input image. Such an adaptive tight frame construction scheme is applied to image denoising by constructing a tight frame tailored to the given noisy data. The experiments showed that the proposed approach performs better in image denoising than those wavelet tight frames designed for a class of images. Moreover, by ensuring that the system derived from our approach is always a tight frame, our approach also runs much faster than other over-complete dictionary based approaches with comparable performance on denoising. © 2013 Elsevier Inc.
Source Title: Applied and Computational Harmonic Analysis
URI: http://scholarbank.nus.edu.sg/handle/10635/103101
ISSN: 1096603X
DOI: 10.1016/j.acha.2013.10.001
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

84
checked on Nov 16, 2018

Page view(s)

44
checked on Oct 5, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.