Abstract
Superresolution (SR) image reconstruction is able to overcome the resolution limit of camera imaging system through integrating information of multiple low resolution (LR) images that the perceived resolution of the reconstruction image is much higher than that of the individual image. Since the ill-posed SR reconstruction problem can be regularized within a Bayesian context by adopting a priori image model, 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 Huber Markov Random Field (HMRF) image prior model and assesses its tuning parameter using maximum likelihood (ML) estimator, finally computes the regularized solution automatically. Hybrid Bayesian estimates computed on simulation images, satellite images and video sequence show dramatic visual and quantitative improvements over bilinear interpolation and Maximum A Posteriori reconstruction results with sharp edges, correctly restored textures and a high PSNR improvement.
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Wang, T., Zhang, Y., Zhang, Y.S. (2006). Hybrid Bayesian Super Resolution Image Reconstruction. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_28
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DOI: https://doi.org/10.1007/978-3-540-37258-5_28
Publisher Name: Springer, Berlin, Heidelberg
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