Decimation estimation and super-resolution using zoomed observations

Authors:
Prakash P. Gajjar;Manjunath V. Joshi;Asim Banerjee;Suman Mitra
Affiliations:
Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India;Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India;Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India;Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India
Venue:
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Year:
2006

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Abstract

We propose a technique for super-resolving an image from several observations taken at different camera zooms. From the set of these images, a super-resolved image of the entire scene (least zoomed) is obtained at the resolution of the most zoomed one. We model the super-resolution image as a Markov Random Field (MRF). The cost function is derived using a Maximum a posteriori (MAP) estimation method and is optimized by using gradient descent technique. The novelty of our approach is that the decimation (aliasing) matrix is obtained from the given observations themselves. Results are illustrated with real data captured using a zoom camera. Application of our technique to multiresolution fusion in remotely sensed images is shown.