SuperResolution image reconstruction using a hybrid bayesian approach

Authors:
Tao Wang;Yan Zhang;Yong Sheng Zhang
Affiliations:
Zhengzhou Institute of Surveying and Mapping, Zhengzhou, China;Zhengzhou Institute of Surveying and Mapping, Zhengzhou, China;Zhengzhou Institute of Surveying and Mapping, Zhengzhou, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 5
Cited 0

Extraction of high-resolution frames from video sequences

IEEE Transactions on Image Processing
Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time

IEEE Transactions on Image Processing
Joint MAP registration and high-resolution image estimation using a sequence of undersampled images

IEEE Transactions on Image Processing
Artifact reduction for set theoretic super resolution image reconstruction with edge adaptive constraints and higher-order interpolants

IEEE Transactions on Image Processing
An adaptive Gaussian model for satellite image deblurring

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.