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Fractional-order sparse representation.for Image Denoising

IEEE/CAA Journal of Automatica Sinica 2018年第2期5卷 555-563页

作者： Leilei Geng Zexuan Ji Yunhao Yuan Yilong Yin School of Computer Science and Technology Shandong University of Finance and Economics School of Computer Science and Engineering Nanjing University of Science and Technology School of Internet of Things Engineering Jiangnan University

sparse representation.models have been shown promising results for image denoising. However, conventional sparse representation.based models cannot obtain satisfactory estimations for sparse coefficients and the dictionary. To address this weakness, in this paper, we propose a novel fractional-order sparse representation.FSR) model. Specifically, we cluster the image patches into K groups, and calculate the singular values for each clean/noisy patch pair in the wavelet domain. Then the uniform fractional-order parameters are learned for each *** a novel fractional-order sample space is constructed using adaptive fractional-order parameters in the wavelet domain to obtain more accurate sparse coefficients and dictionary for image denoising. Extensive experimental results show that the proposed model outperforms state-of-the-art sparse representation.based models and the block-matching and 3D filtering algorithm in terms of denoising performance and the computational efficiency.

关键词： Index Terms-Fractional-order image denoising multi-scale sparse representation.

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Target localization for MIMO radar with unknown mutual coupling based on sparse representation

Target localization for MIMO radar with unknown mutual coupl...

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第五届中国卫星导航学术年会

作者： Jianfeng Li Xiaofei Zhang College of electronic and information engineering Nanjing University of Aeronautics and Astronautics（NUAA）

Multiple-input multiple-output（MIMO） radars have attracted lots of attention for their special *** a key issue,target localization method for MIMO radar has also been studied by lots of ***,most of these methods are based on the ideal array model,and the practical unknown mutual coupling will make them have great performance degradation or even fail to *** representation.SR） has obtained a rapid development in the field of array signal processing over the past few years for its loose requirement for data amount and high-resolution *** only one-dimensional angle is considered,and the unknown mutual coupling will make the dictionary hard to construct when apply it for MIMO radar target *** if the mutual coupling is compensated,the two-dimensional angle in MIMO radar will make the dictionary very huge,as well as enhance the inter-correlation of the *** a result,it brings higher complexity without performance ***,according to our study,both the influences of mutual coupling and two-dimensional angle are occurred in space domain,but the output data of MIMO radar contains both the information in space and time ***,the influenced space domain is avoided and Doppler frequency in the time domain is utilized in this paper and not only the mutual coupling is avoided,but also a one-dimensional dictionary for the Doppler frequency can be *** Doppler frequency can be firstly obtained via the sparse recovery technique,and meanwhile,the influenced direction matrix can be acquired from the non-zero rows of the recovered *** to the special characteristic of the mutual coupling in uniform linear array（ULA）,select the special rows of the direction matrix to eliminate the effect of mutual coupling and estimate the *** estimated target parameters（two-dimensional angles and Doppler frequency） are automatically paired,and the proposed algorithm has better estimation perfo

关键词： MIMO radar unknown mutual coupling Target localization sparse representation.

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