Fast Huber-Markov edge-preserving image restoration

Ruimin Pan; Stanley J. Reeves

doi:10.1117/12.587823

11 March 2005 Fast Huber-Markov edge-preserving image restoration

Ruimin Pan, Stanley J. Reeves

Proceedings Volume 5674, Computational Imaging III; (2005) https://doi.org/10.1117/12.587823
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

Abstract

In general, image restoration problems are ill posed and need to be regularized. For applications such as realtime video, fast restorations are also needed to keep up with the frame rate. Restoration based on 2D FFT's provides a fast implementation assuming a constant regularization term over the image. Unfortunately, this assumption creates significant ringing artifacts on edges as well as blurrier edges in the restored image. On the other hand, shift-variant regularization will reduce edge artifacts and provide better quality but it destroys the structure that makes use of the 2D FFT possible, thus no longer have the computational efficiency of the FFT. In this paper, we use a Bayesian approach-maximum a posteriori (MAP) estimation to compute an estimate of the original image given the blurred image. To avoid the smoothing of edges, shift-variant regularization must be used. The Huber-Markov random field model is applied to preserve the discontinuities on edges. For fast minimization of the above model, a new algorithm involving the Sherman-Morrison matrix inversion lemma is proposed. This results in a restored image with good edge preservation and less computation. Experiments show restored images with sharper edges. Convergence is fast, and the computational speed can be improved considerably by breaking the image into subimages.

Citation Download Citation

Ruimin Pan and Stanley J. Reeves "Fast Huber-Markov edge-preserving image restoration", Proc. SPIE 5674, Computational Imaging III, (11 March 2005); https://doi.org/10.1117/12.587823

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