Bayesian multi frame super resolution

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2014

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Turgay, Emre

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This thesis aims at increasing the eﬀective resolution of an image using a set of low resolution images. This process is referred to as super resolution (SR) image reconstruction in the literature. This work proposes maximum a-posteriori (MAP) based iterative reconstruction methods for this problem. The ﬁrst contribution of the thesis is a novel edge preserving SR image reconstruction method. The proposed MAP based estimator uses local gradient direction and amplitude for optimal noise reduction while preserving edges. The second contribution of the thesis is a novel texture prior for maximum a posteriori (MAP) based super resolution (SR) image reconstruction. The prior is based on a multiscale compound Markov Random Field (MRF) model. Gabor ﬁlters are utilized for subband decomposition. Each subband is modeled by a compound MRF that inherits a binary texture process. The texture process at each pixel location at each subband is estimated iteratively along with the unknown high-resolution image pixels. Finally, a two stage SR method comprising a Bayesian reconstruction step followed by a restoration step is proposed. In the ﬁrst stage, two MAP based SR estimators with diﬀerent regularizations are employed. In the second stage, pixel-to-pixel diﬀerence between these two estimates is post-processed to restore edges and textures while eliminating noise. Experiments on synthetically generated images and real experiments on visual CCD cameras and thermal cameras demonstrate that the proposed methods are more favorable compared to state-of-the-art SR methods especially on textures and edges.

Subject Keywords

Image reconstruction., High resolution imaging., Image processing, Imaging systems

URI

http://etd.lib.metu.edu.tr/upload/12616728/index.pdf
https://hdl.handle.net/11511/23259

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

E. Turgay, “Bayesian multi frame super resolution,” Ph.D. - Doctoral Program, Middle East Technical University, 2014.