Fusion of noisy images based statistical model in shearlet domain

Chengzhi Deng; Xin Hu

doi:10.1117/12.901982

23 November 2011 Fusion of noisy images based statistical model in shearlet domain

Chengzhi Deng, Xin Hu

Proceedings Volume 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 80060Y (2011) https://doi.org/10.1117/12.901982
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

A new image fusion approach based on the modeling of shearlet coefficients with normal inverse gaussian model is proposed. The approach focus on the fusion of noisy images. Based on the statistical model and additive white gaussian noise, an subband adaptive shrinkage function is derived by using the maximum a posteriori rule. And then, the new scheme for shearlet-domain image fusion is proposed by incorporating the adaptive shrinkage rule into the fusion scheme. Experimental results show the proposed method perfor very well with noisy images, outperform other conventional methods.

Citation Download Citation

Chengzhi Deng and Xin Hu "Fusion of noisy images based statistical model in shearlet domain", Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 80060Y (23 November 2011); https://doi.org/10.1117/12.901982

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available