Unified regularization framework for blind image super-resolution

Yuanxu Chen; Yupin Luo; Dongcheng Hu

doi:10.1117/1.2817219

1 December 2007 Unified regularization framework for blind image super-resolution

Yuanxu Chen, Yupin Luo, Dongcheng Hu

Author Affiliations +

Optical Engineering, Vol. 46, Issue 12, 127001 (December 2007). https://doi.org/10.1117/1.2817219

Abstract

Blind superresolution (BSR) is one of the challenges in image superresolution. We propose a new approach using a unified regularization framework, which solves image registration, point spread function (PSF) estimation, and high-resolution (HR) image reconstruction simultaneously. To achieve this, the anisotropic diffusion techniques are employed as one regularization term to preserve edge information in the HR image estimation, and a generalized version of the eigenvector-based (EVAM) constraint is developed to regularize the PSF. An alternating minimization algorithm is devised to find optimal solutions, and an effective numerical implementation scheme, based on local filtering, is proposed to suppress the ringing artifacts in the image reconstruction. Finally, experiments with synthetic and real data are presented to demonstrate the effectiveness and robustness of our approach, which can handle motion blur well and enhance resolution notably for very noisy images.

©(2007) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Yuanxu Chen, Yupin Luo, and Dongcheng Hu "Unified regularization framework for blind image super-resolution," Optical Engineering 46(12), 127001 (1 December 2007). https://doi.org/10.1117/1.2817219

Published: 1 December 2007

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