Super-resolution enhancement of text image sequences

Conference item

The objective of this work is the super-resolution enhancement of image sequences. We consider in particular images of scenes for which the point-to-point image transformation is a plane projective transformation. We first describe the imaging model, and a maximum likelihood (ML) estimator of the super-resolution image. We demonstrate the extreme noise sensitivity of the unconstrained ML estimator. We show that the Irani and Peleg (1991, 1993) super-resolution algorithm does not suffer from this sensitivity, and explain that this stability is due to the error back-projection method which effectively constrains the solution. We then propose two estimators suitable for the enhancement of text images: a maximum a posteriori (MAP) estimator based on a Huber prior and an estimator regularized using the total variation norm. We demonstrate the improved noise robustness of these approaches over the Irani and Peleg estimator. We also show the effects of a poorly estimated point spread function (PSF) on the super-resolution result and explain conditions necessary for this parameter to be included in the optimization. Results are evaluated on both real and synthetic sequences of text images. In the case of the real images, the projective transformations relating the images are estimated automatically from the image data, so that the entire algorithm is automatic.

Authors: Capel, D; Zisserman, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000
• Volume: 1
• Pages: 600-605
• Publication date: 2002-08-06
• Event title: 15th International Conference on Pattern Recognition (ICPR2000)
• Event location: Barcelona, Spain
• Event start date: 2000-09-03
• Event end date: 2000-09-08
• DOI: 10.1109/icpr.2000.905409
• ISSN: 1051-4651
• ISBN: 0769507506
• Language: English
• Keywords: spatial resolution; image resolution; Bayesian methods; layout; maximum likelihood estimation; geometrical optics; degradation; optical noise; image sequences; optical imaging