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Journal of Software Engineering
  Year: 2015 | Volume: 9 | Issue: 3 | Page No.: 520-533
DOI: 10.3923/jse.2015.520.533
Region Adaptive Super Resolution Based on Total Variational Regularization
Haitian Zhai, Hui Li and Mingui Sun

Abstract:
Super-resolution is the process of combining a sequence of low-resolution images to produce a higher resolution image. The conventional super-resolution algorithms usually apply the same regularization factor for the whole image, regardless of region characteristics. One regularization factor is not good enough for all regions since an image consists of various regions having different characteristics. In this study, we propose a region adaptive super-resolution method to apply an adaptive regularization factor for each separate region. The regions are generated by segmenting the reference frame using the improved hierarchical segmentation algorithm. Regularization parameters are then adaptively determined based on the region characteristics. The software of the proposed algorithm is also implemented on an Intel quad core computer. Finally, the experimental results using both synthetic and unsynthetic image sequences show the effectiveness of the proposed algorithm compared to three state-of-the-art super resolution algorithms.
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How to cite this article:

Haitian Zhai, Hui Li and Mingui Sun, 2015. Region Adaptive Super Resolution Based on Total Variational Regularization. Journal of Software Engineering, 9: 520-533.

DOI: 10.3923/jse.2015.520.533

URL: https://scialert.net/abstract/?doi=jse.2015.520.533

 
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