Total variation blind deconvolution
- 1 March 1998
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 7 (3), 370-375
- https://doi.org/10.1109/83.661187
Abstract
In this paper, we present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed. 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 psf's without sharp edges, e.g., Gaussian blur, can also be identified through the TV approach.This publication has 11 references indexed in Scilit:
- Anisotropic blind image restorationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Total variation blind deconvolutionIEEE Transactions on Image Processing, 1998
- Recovery of Blocky Images from Noisy and Blurred DataSIAM Journal on Applied Mathematics, 1996
- A regularization approach to joint blur identification and image restorationIEEE Transactions on Image Processing, 1996
- Iterative Methods for Total Variation DenoisingSIAM Journal on Scientific Computing, 1996
- Blind deconvolution by means of the Richardson–Lucy algorithmJournal of the Optical Society of America A, 1995
- Analysis of bounded variation penalty methods for ill-posed problemsInverse Problems, 1994
- Nonlinear total variation based noise removal algorithmsPhysica D: Nonlinear Phenomena, 1992
- Iterative Identification and Restoration of ImagesPublished by Springer Science and Business Media LLC ,1991
- Identification and restoration of noisy blurred images using the expectation-maximization algorithmIEEE Transactions on Acoustics, Speech, and Signal Processing, 1990