Decimation Estimation and Super-Resolution Using Zoomed Observations

• Published in: Computer Vision, Graphics and Image Processing pp. 45 - 57
• Main Authors: Gajjar, Prakash P., Joshi, Manjunath V., Banerjee, Asim, Mitra, Suman
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2006
• Series: Lecture Notes in Computer Science
• Subjects: High Resolution Image; Markov Random Field; Markov Random Field Model; Mean Square Error; Zoom Factor
• ISBN: 3540683011; 9783540683018
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11949619_5

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.