SuperResolution Image Reconstruction Using a Hybrid Bayesian Approach
There are increasing demands for high-resolution (HR) images in various applications. Image superresolution (SR) reconstruction refers to methods that increase image spatial resolution by fusing information from either a sequence of temporal adjacent images or multi-source images from different sensors. In the paper we propose a hybrid Bayesian method for image reconstruction, which firstly estimates the unknown point spread function(PSF) and an approximation for the original ideal image, and then sets up the HMRF image prior model and assesses its tuning parameter using maximum likelihood estimator, finally computes the regularized solution automatically. Hybrid Bayesian estimates computed on actual satellite images and video sequence show dramatic visual and quantitative improvements in comparison with the bilinear interpolation result, the projection onto convex sets (POCS) estimate and Maximum A Posteriori (MAP) estimate.
KeywordsPoint Spread Function Bilinear Interpolation Blind Deconvolution Newton Direction SuperResolution Image
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