Bayesian multi frame super resolution

Turgay, Emre
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.


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