Image super-resolution based on image adaptive decomposition

Qiwei Xie; Haiyan Wang; Lijun Shen; Xi Chen; Hua Han

doi:10.1117/12.911893

5 December 2011 Image super-resolution based on image adaptive decomposition

Qiwei Xie, Haiyan Wang, Lijun Shen, Xi Chen, Hua Han

Proceedings Volume 8005, MIPPR 2011: Parallel Processing of Images and Optimization and Medical Imaging Processing; 80050N (2011) https://doi.org/10.1117/12.911893
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

In this paper we propose an image super-resolution algorithm based on Gaussian Mixture Model (GMM) and a new adaptive image decomposition algorithm. The new image decomposition algorithm uses local extreme of image to extract the cartoon and oscillating part of image. In this paper, we first decompose an image into oscillating and piecewise smooth (cartoon) parts, then enlarge the cartoon part with interpolation. Because GMM accurately characterizes the oscillating part, we specify it as the prior distribution and then formulate the image super-resolution problem as a constrained optimization problem to acquire the enlarged texture part and finally we obtain a fine result.

Citation Download Citation

Qiwei Xie, Haiyan Wang, Lijun Shen, Xi Chen, and Hua Han "Image super-resolution based on image adaptive decomposition", Proc. SPIE 8005, MIPPR 2011: Parallel Processing of Images and Optimization and Medical Imaging Processing, 80050N (5 December 2011); https://doi.org/10.1117/12.911893

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