Translator Disclaimer
1 September 2010 Single-image motion deblurring using adaptive anisotropic regularization
Author Affiliations +
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)
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
JOURNAL ARTICLE
13 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Single image blind motion deblurring
Proceedings of SPIE (August 29 2016)
Restoration of depth-based space-variant blurred images
Proceedings of SPIE (September 06 2019)
Evaluation of the PSF from motion-blurred images
Proceedings of SPIE (September 22 1997)

Back to Top