An Adaptive Parameter Estimation in a BTV Regularized Image Super-Resolution Reconstruction

Access to the fine spatial resolution has always been a hotspot in digital imaging. One way to improve resolution is to use signal post-processing techniques. In this study, an improved multi-frame image super-resolution (SR) algorithm is proposed. The objective function should be minimized consists...

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Bibliographic Details
Published in:Advances in electrical and computer engineering 2017-08, Vol.17 (3), p.3-10
Main Authors: MOFIDI, M., HAJGHASSEM, H., AFIFI, A.
Format: Article
Language:English
Subjects:
Algorithms
Digital imaging
Errors
image processing
Image reconstruction
Image resolution
maximum a posteriori
Methods
Noise
Parameter estimation
Post-processing
Regularization
Signal processing
Spatial resolution
statistical analysis
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Summary:Access to the fine spatial resolution has always been a hotspot in digital imaging. One way to improve resolution is to use signal post-processing techniques. In this study, an improved multi-frame image super-resolution (SR) algorithm is proposed. The objective function should be minimized consists of a data error term, a regularization term and a regularization parameter. Based on the bilateral-totalvariation (BTV) regularization, in the proposed method on one hand, the data error term incorporates frames with high accuracies in the reconstruction process, where an indicator weights each frame proportional to the frame error. On the other hand the regularization parameter is updated in each iteration based upon the Morozovs discrepancy principle. Iterative adjustment of the regularization parameter guarantees the SR solution to satisfy discrepancy principle. Visual evaluation and also quantitative measurements show that the performance of the proposed algorithm is better than of the several state-of-the-art methods.
ISSN:1582-7445
1844-7600
DOI:10.4316/AECE.2017.03001