Super-Resolution Using Hidden Markov Model and Bayesian Detection Estimation Framework
- 1.2k Downloads
This paper presents a new method for super-resolution (SR) reconstruction of a high-resolution (HR) image from several low-resolution (LR) images. The HR image is assumed to be composed of homogeneous regions. Thus, the a priori distribution of the pixels is modeled by a finite mixture model (FMM) and a Potts Markov model (PMM) for the labels. The whole a priori model is then a hierarchical Markov model. The LR images are assumed to be obtained from the HR image by lowpass filtering, arbitrarily translation, decimation, and finally corruption by a random noise. The problem is then put in a Bayesian detection and estimation framework, and appropriate algorithms are developed based on Markov chain Monte Carlo (MCMC) Gibbs sampling. At the end, we have not only an estimate of the HR image but also an estimate of the classification labels which leads to a segmentation result.
KeywordsInformation Technology Markov Chain Markov Model Mixture Model Hide Markov Model
- 2.Argyriou V, Vlachos T: Sub-pixel motion estimation using gradient cross-correlation. Proceedings of 7th IEEE International Symposium on Signal Processing and its Applications (ISSPA '03), July 2003, Paris, France 2: 215–218.Google Scholar
- 3.Humblot F, Collin B, Mohammad-Djafari A: Evaluation and practical issues of subpixel image registration using phase correlation methods. Proceedings of Physics in Signal and Image Processing (PSIP '05), January–February 2005, Toulouse, FranceGoogle Scholar
- 6.Tsai RY, Huang TS: Multi-frame image restoration and registration. Advances in Computer Vision on Image Processing 1984, 1: 317–339.Google Scholar
- 7.Schulz TJ: Multiframe image restoration. In Handbook of Image and Video Processing. Edited by: Bovik A. Academic Press, New York, NY, USA; 2000:175–189. chapter 3.8Google Scholar
- 8.Borman S: Topics in multiframe super-resolution restoration, M.S. thesis. University of Notre Dame, Notre Dame, Ind, USA; May 2004.Google Scholar
- 15.Snoussi H, Mohammad-Djafari A: Information geometry and prior selection. In Proceedings of 22nd International workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt '02), August 2002, Moscow, Idaho, USA Edited by: Williams CJ. 307–327.Google Scholar
- 16.Snoussi H: A Bayesian approach to source separation. Applications in imagery, M.S. thesis. University of Paris–Sud, Orsay, France; September 2003.Google Scholar
- 18.Rochefort G, Champagnat F, Le Besnerais G, Giovannelli J-F: Super-resolution from a sequence of undersampled image under affine motion. submitted to in IEEE Transactions on Image Processing, March 2005Google Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.