2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-30.1
Paper Title NON-CONVEX SPARSE DEVIATION MODELING VIA GENERATIVE MODELS
Authors Yaxi Yang, Hailin Wang, Southwest University, China; Haiquan Qiu, Xi’an Jiaotong University, China; Jianjun Wang, Southwest University, China; Yao Wang, Xi’an Jiaotong University, China
SessionIVMSP-30: Inverse Problems in Image & Video Processing
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In this paper, the generative model is used to introduce the structural properties of the signal to replace the common sparse hypothesis, and a non-convex compressed sensing sparse deviation model based on the generative model ($\ell_q$-Gen) is proposed. By establishing $\ell_q$ variant of the restricted isometry property ($q$-RIP) and Set-Restricted Eigenvalue Condition ($q$-$S$-REC), the error upper bound of the optimal decoder is derived when the recovered signal is within the sparse deviation range of the generator. Furthermore, it is proved that the Gaussian matrix satisfying a certain number of measurements is sufficient to ensure a good recovery for the generating function with high probability. Finally, a series of experiments are carried out to verify the effectiveness and superiority of the $\ell_q$-Gen model.