Single-image motion deblurring using adaptive anisotropic regularization

Hanyu Hong; In Kyu Park

doi:10.1117/1.3487743

1 September 2010 Single-image motion deblurring using adaptive anisotropic regularization

Hanyu Hong, In Kyu Park

Author Affiliations +

Optical Engineering, Vol. 49, Issue 9, 097008 (September 2010). https://doi.org/10.1117/1.3487743

Abstract

We present a novel algorithm to remove motion blur from a single blurred image. To estimate the unknown motion blur kernel as accurately as possible, we propose an adaptive algorithm using anisotropic regularization. The proposed algorithm preserves the point spread function (PSF) path while keeping the properties of the motion PSF when solving for the blur kernel. Adaptive anisotropic regularization and refinement of the blur kernels are incorporated into an iterative process to improve the precision of the blur kernel. Maximum likelihood (ML) estimation deblurring based on edge-preserving regularization is derived to reduce artifacts while avoiding oversmoothing of the details. By using the estimated blur kernel and the proposed ML estimation deblurring, the motion blur can be removed effectively. The experimental results for real motion blurred images show that the proposed algorithm can removes motion blur effectively for a variety of real scenes.

©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Hanyu Hong and In Kyu Park "Single-image motion deblurring using adaptive anisotropic regularization," Optical Engineering 49(9), 097008 (1 September 2010). https://doi.org/10.1117/1.3487743

Published: 1 September 2010

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