Image super-resolution enhancement based on online learning and blind sparse decomposition
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 superresolution 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.
Sparse decomposition sparse representation dictionary learning super-resolution reconstruction
Jinzheng Lu Qiheng Zhang Zhiyong Xu Zhenming Peng
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China School of Op Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China School of Optoelectronic Information, University of Electronic Science and Technology of China,Cheng
国际会议
桂林
英文
1-8
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）