Total variation blind deconvolution

Author

Chan, Tony F. ; Wong, Chiu-Kwong

Author_Institution

Dept. of Math., California Univ., Los Angeles, CA, USA

Volume

7

Issue

3

fYear

1998

fDate

3/1/1998 12:00:00 AM

Firstpage

370

Lastpage

375

Abstract

We present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed by Acar and Vogel (1994). The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images as well as some blurring functions, e.g., motion blur and out-of-focus blur. An alternating minimization (AM) implicit iterative scheme is devised to recover the image and simultaneously identify the point spread function (PSF). Numerical results indicate that the iterative scheme is quite robust, converges very fast (especially for discontinuous blur), and both the image and the PSF can be recovered under the presence of high noise level. Finally, we remark that PSFs without sharp edges, e.g., Gaussian blur, can also be identified through the TV approach

Keywords

convergence of numerical methods; deconvolution; edge detection; iterative methods; minimisation; optical transfer function; Gaussian blur; alternating minimization; blind deconvolution algorithm; blurring functions; convergence; discontinuous blur; high noise level; image edge recovery; implicit iterative scheme; motion blur; out-of-focus blur; point spread function; total variation blind deconvolution; total variational minimization method; total variational norm; Convolution; Deconvolution; Gradient methods; Image converters; Iterative algorithms; Mathematics; Minimization methods; Noise level; Noise robustness; TV;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.661187

Filename

661187