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Guilin, China

Han X.,Tsinghua University | Han X.,Guilin Airforce Academy | Zhang H.,Tsinghua University | Meng H.,Tsinghua University
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2011

In this paper, we propose a new algorithm, TLS-FOCUSS, for sparse recovery for large underdetermined linear systems, based on total least square (TLS) method and FOCUSS(FOCal Underdetermined System Solver). The problem of sparse recovery when perturbations appear in both the measurements and the dictionary (sensing matrix) is considered. FOCUSS algorithm is extended with main idea of TLS to reduce the impact of the perturbation of dictionary on the performance of sparse recovery. The simulation results illustrate the advantage of TLS-FOCUSS on accuracy and stability compared with ordinary FOCUSS algorithm. © 2011 IEEE.


Li M.,University of Electronic Science and Technology of China | Li M.,Guilin Airforce Academy | Cheng J.,University of Electronic Science and Technology of China | Li X.-W.,University of Electronic Science and Technology of China | Le X.,University of Electronic Science and Technology of China
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | Year: 2011

A novel learning-based image inpainting method is presented. As a further development of classical sparse representation model, the non-local self-similar patches are unified for joint sparse representation and learning dictionary, in which each element of the self-similar patches has the same sparse pattern. The method assures the self-similar patches possess similarity when projected on the sparse space, and efficiently builds the sparse association among them. This association is next taken as a priori knowledge for image inpainting. The paper uses numerous samples and non-local patches of input image to train overcomplete dictionary. The method not only takes into account the priori knowledge of samples, but also considers the non-local self-similar information of input image. Large and small region inpainting experiments and text removing experiments on natural images show the good performance of the method.


Li M.,University of Electronic Science and Technology of China | Li M.,Guilin Airforce Academy | Li S.,University of Electronic Science and Technology of China | Le X.,University of Electronic Science and Technology of China | And 2 more authors.
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | Year: 2011

Most recent image inpainting methods only use valid information found in input image as the clue to fill the inpainting region. These methods usually have the defects of insufficient prior information and relatively poor adaptivity. A novel learned dictionary based image inpainting framework is presented. The key idea is to build a sparse relationship between raw image patches and their corresponding feature patches, then use this relationship as the priori to guide the inpainting. Our method not only uses the valid information of the input image itself, but also utilizes the prior information of the sample images to improve the adaptivity. Large and small region inpainting experiments and text removing experiments on nature images show the good performance of our method.


Ren S.,University of Electronic Science and Technology of China | Cheng J.,University of Electronic Science and Technology of China | Li M.,Guilin Airforce Academy
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2010

Image fusion can enhance remote sensing image data information so that the fused image is more in line with the vision of the human and better to analysis and processing of the image. How to fuse the multispectral images with a high spectral and the panchromatic image with a high spatial making the fused images with a high spectral as well as a high spatial resolution is recently thought to be especially important in remote sensing image study. Image fusion based on the traditional methods are not ideal for describing the images with high dimensional singularities. In this paper, we proposed a remote sensing image fusion algorithm based on the Curvelet transform, which represents the image edges better and is anisotropy We have experimented with both IKONOS images of Wenchuan in Sichuan province after the 5.12 earthquake and a group of resource satellite images to testify the performance of the method. © 2010 IEEE.


Li M.,University of Electronic Science and Technology of China | Li M.,Guilin Airforce Academy | Cheng J.,University of Electronic Science and Technology of China | Le X.,University of Electronic Science and Technology of China | And 2 more authors.
Guangdian Gongcheng/Opto-Electronic Engineering | Year: 2011

A super-resolution method based on sparse dictionary is presented. The method efficiently builds sparse association between high-frequency components of HR image patches and LR image feature patches, and defines the association as a prior knowledge to guide super-resolution reconstruction based on sparse dictionary. Compared with overcomplete dictionary, sparse dictionary is more compact and effective to express the prior knowledge. We choose the high-frequency component of the HR image patch as its feature for dictionary training, which builds the sparse association between LR image patches and HR ones with better efficiency and less training examples. Sparse K-SVD algorithm is adopted as optimization method to improve the computation efficiency. Experiments with natural images show that our method outperforms several other learning-based super-resolution algorithms.

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