Interscale Stein's Unbiased Risk Estimate and Intrascale Feature Patches Distance Constraint for Image Denoising

Qieshi ZHANG  Sei-ichiro KAMATA  Alireza AHRARY  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E93-A   No.8   pp.1434-1441
Publication Date: 2010/08/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E93.A.1434
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Signal Processing)
Category: Image
Keyword: 
image denoising,  orthonormal Wavelet transform,  Stein's unbiased risk estimate (SURE),  feature patches distance constraint (FPDC),  

Full Text: PDF>>
Buy this Article




Summary: 
The influence of noise is an important problem on image acquisition and transmission stages. The traditional image denoising approaches only analyzing the pixels of local region with a moving window, which calculated by neighbor pixels to denoise. Recently, this research has been focused on the transform domain and feature space. Compare with the traditional approaches, the global multi-scale analyzing and unchangeable noise distribution is the advantage. Apparently, the estimation based methods can be used in transform domain and get better effect. This paper proposed a new approach to image denoising in orthonormal wavelet domain. In this paper, we adopt Stein's unbiased risk estimate (SURE) based method to denoise the low-frequency bands and the feature patches distance constraint (FPDC) method also be proposed to estimate the noise free bands in Wavelet domain. The key point is that how to divide the lower frequency sub-bands and the higher frequency sub-bands, and do interscale SURE and intrascale FPDC, respectively. We compared our denoising method with some well-known and new denoising algorithms, the experimental results show that the proposed method can give better performance and keep more detail information in most objective and subjective criteria than other methods.