Translator Disclaimer
2 December 2011 Image super-resolution enhancement based on online learning and blind sparse decomposition
Author Affiliations +
Proceedings Volume 8004, MIPPR 2011: Pattern Recognition and Computer Vision; 80040B (2011)
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China
This paper presents a different learning-based image super-resolution enhancement method based on blind sparse decomposition, in order to improve its resolution of a degraded one. Firstly, sparse decomposition based image super-resolution enhancement model is put forward according to the geometrical invariability of local image structures under different conditions of resolution. Secondly, for reducing the complexity of dictionary learning and enhancing adaptive representation ability of dictionary atoms, the over-complete dictionary is constructed using online learning fashion of the given low resolution image. Thirdly, since the fixed sparsity of the conventional matching pursuit algorithms for sparse decomposition can not fit all types of patches, the approach to sparse decomposition with blind sparsity can achieve relatively higher accurate sparse representation of an image patch. Lastly, atoms of high resolution dictionary and coefficients of representation of the given low-resolution image are synthesized to the desired SR image. Experimental results of the synthetic and real data demonstrate that the suggested framework can eliminate blurring degradation and annoying edge artifacts in the resulting images. The proposed method can be effectively applied to resolution enhancement of the single-frame low-resolution image.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jinzheng Lu, Qiheng Zhang, Zhiyong Xu, and Zhenming Peng "Image super-resolution enhancement based on online learning and blind sparse decomposition", Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 80040B (2 December 2011);

Back to Top