Compound L0 regularization method for image blind motion deblurring

Qiaohong Liu; Liping Sun; Zeguo Shao

doi:10.1117/1.JEI.25.5.053013

20 September 2016 Compound L₀ regularization method for image blind motion deblurring

Qiaohong Liu, Liping Sun, Zeguo Shao

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 5, 053013 (September 2016). https://doi.org/10.1117/1.JEI.25.5.053013

Abstract

Blind image deblurring is one of the challenging problems in image processing and computer vision. The main purpose of blind image deblurring is to estimate the correct blur kernel and restore the latent image with edge-preservation, details-protection, and ringing suppression. In order to achieve ideal results, an innovative compound L₀-regularized model is proposed to estimate the blur kernel by regularizing the sparsity property of natural images and two characteristics of blur kernel, such as continuity and sparsity. In the alternating direction framework, the split Bregman algorithm and half-quadratic splitting rule are alternatively employed to optimize the proposed kernel estimation model. Finally, a nonblind restoration method with ringing suppression is developed to obtain the ultimate latent image. Extensive experiments demonstrate the efficiency and viability of the proposed method compared with some state-of-the-art blind deblurring methods.

Citation Download Citation

Qiaohong Liu, Liping Sun, and Zeguo Shao "Compound L₀ regularization method for image blind motion deblurring," Journal of Electronic Imaging 25(5), 053013 (20 September 2016). https://doi.org/10.1117/1.JEI.25.5.053013

Published: 20 September 2016

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