4 Papers
9 Citations
Li Bei is an academic researcher from University of Shanghai for Science and Technology. The author has contributed to research in topics: Compressed sensing & Matrix (mathematics). The author has an hindex of 2, co-authored 4 publications.
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Papers
Recovery of a spectrum based on a compressive-sensing algorithm with weighted principal component analysis
Abstract: The purpose of this study is to improve the reconstruction precision and better copy the color of spectral image surfaces. A new spectral reflectance reconstruction algorithm based on an iterative threshold combined with weighted principal component space is presented in this paper, and the principal component with weighted visual features is the sparse basis. Different numbers of color cards are selected as the training samples, a multispectral image is the testing sample, and the color differences in the reconstructions are compared. The channel response value is obtained by a Mega Vision high-accuracy, multi-channel imaging system. The results show that spectral reconstruction based on weighted principal component space is superior in performance to that based on traditional principal component space. Therefore, the color difference obtained using the compressive-sensing algorithm with weighted principal component analysis is less than that obtained using the algorithm with traditional principal component analysis, and better reconstructed color consistency with human eye vision is achieved.
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The study of key technology on spectral reflectance reconstruction based on the algorithm of adaptive compressive sensing
TL;DR: In order to improve the reconstruction accuracy and reduce the workload, the algorithm of compressive sensing based on the iterative threshold is combined with the method of adaptive selection of the training sample, and a new algorithm of adaptivecompressive sensing is put forward.
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Study on the key technology of spectral reflectance reconstruction based on the weighted measurement matrix
TL;DR: In this article, a new method of spectral reflectance reconstruction based on the weighted measurement matrix is proposed, which is a combination of three kinds of common reflectance reconstructions, which are the pseudo inverse method, the Wiener estimation method and the principal component analysis method.
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Study of key technology of ghost imaging via compressive sensing for a phase object based on phase-shifting digital holography
TL;DR: In this article, the authors used compressive sensing to improve the imaging resolution and realize the phase distribution of a phase object based on the theoretical analysis of the lensless Fourier imaging of the algorithm of ghost imaging based on phase shifting digital holography.
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