Wavelet-Based Total Variation and Nonlocal Similarity Model for Image Denoising

To suppress the heavy noise and keep the distinct edges of the images in the low light condition, we propose a denoising model based on the combination of total variation (TV) and nonlocal similarity in the wavelet domain. The TV regularization in the wavelet domain effectively suppresses the heavy...

Full description

Saved in:
Bibliographic Details
Published in:IEEE signal processing letters 2017-06, Vol.24 (6), p.877-881
Main Authors: Shen, Yan, Liu, Qing, Lou, Shuqin, Hou, Ya-Li
Format: Article
Language:English
Subjects:
Birothogonal wavelet
heavy noise
Image edge detection
Image restoration
Noise reduction
nonlocal similarity
split Bregman
total variation (TV)
Wavelet coefficients
Wavelet domain
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To suppress the heavy noise and keep the distinct edges of the images in the low light condition, we propose a denoising model based on the combination of total variation (TV) and nonlocal similarity in the wavelet domain. The TV regularization in the wavelet domain effectively suppresses the heavy noise with the biorthogonal wavelet function; the nonlocal similarity regularization improves the fine image details. Denoising experiments on artificially degraded and low light images show that in the heavy noise condition, the proposed denoising model can suppress the heavy noise effectively and preserve the detail of images than several state-of-the-art methods.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2017.2688707