Image denoising by principal basis analysis

Hong Sun; Cheng-Wei Sang; Cheng-Guang Liu

doi:10.1117/12.2205535

17 December 2015 Image denoising by principal basis analysis

Hong Sun, Cheng-Wei Sang, Cheng-Guang Liu

Proceedings Volume 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis; 98110K (2015) https://doi.org/10.1117/12.2205535
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

This article addresses the image denoising problem in the situations of strong noise. The method we propose is intended to preserve faint signal details under these difficult circumstances. The new method we introduce, called principal basis analysis, is based on a novel criterion: the reproducibility which is an intrinsic characteristic of the geometric regularity in natural images. We show how to measure reproducibility. Then we present the principal basis analysis method, which chooses, in sparse representation of the signal, the components optimizing the reproducibility degree to build a so-called principal basis. With this principal basis, we show that a noise-free reconstruction may be obtained. As illustrations, we apply the principal signal basis to image denoising for natural images with details in low signal-to-noise ratio, showing performance better than some reference methods.

Citation Download Citation

Hong Sun, Cheng-Wei Sang, and Cheng-Guang Liu "Image denoising by principal basis analysis", Proc. SPIE 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis, 98110K (17 December 2015); https://doi.org/10.1117/12.2205535

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