Needell, "Compressed sensing and dictionary learning," Finite Frame Theory: A Complete Introduction to Overcompleteness, vol. 73, p. 201, January 2015.G. Chen and D. Needell. Compressed sensing and dictionary learning. Preprint, 2015.Guangliang Chen and Deanna Needell. Compressed sensing and ...
Commercial shortwave-infrared cameras based upon compressed sensing are available. 目前,基于压缩感知技术的商用短红外相机已被推出 。 LASER-wikipedia2 Compressed sensing method and device 一种压缩感知方法及装置 patents-wipo One of the most important applications of sparse dictionary learning is in...
Dear editor,Compressed sensing(CS) [1], as an efficient data acquisition paradigm, has attracted much attentions since it came up. The fundamental principle of CS is that a signal, which is sparse under some sparsity basis, can be efficiently acquired and accurately recovered via far fewer ...
Weitong Zhang, in Brain and Nature-Inspired Learning Computation and Recognition, 2020 1.4.1 The development of compressive sensing Compressed sensing (CS) [1, 2] is a new framework about signal acquisition and sensors, and the development of its theory and technology has a profound impact on ...
This paper addresses the problem of simultaneous signal recovery and dictionary learning based on compressive measurements. Multiple signals are analyzed jointly, with multiple sensing matrices, under the assumption that the unknown signals come from a union of a small number of disjoint subspaces. This...
deareditor Compressed sensing (CS) [1], as an efficient data acquisition paradigm, has attracted much attentions since it came up. The fundamental principle of CS is that a signal, which is sparse under some sparsity basis, can be efficiently acquired and accurately recovered via far fewer ...
In this paper, we have explored the framework of compressed sensing (CS) and sparse representation (SR) to reduce the footprint of unit selection based speech synthesis (USS) system. In the CS based framework, footprint reduction is achieved by storing either CS measurements or signs of CS ...
Performance guarantees for recovery algorithms employed in sparse representations, and compressed sensing highlights the importance of incoherence. Optimal bounds of incoherence are attained by equiangular unit norm tight frames (ETFs). Although ETFs are important in many applications, they do not exist fo...
Computer Science - LearningStatistics - Machine LearningBlind Compressed Sensing (BCS) is an extension of Compressed Sensing (CS) where the optimal sparsifying dictionary is assumed to be unknown and subject to estimation (in addition to the CS sparse coefficients). Since the emergence of BCS, ...
According to the compressed sensing magnetic resonance fast imaging method, the signal sparsity is improved through dictionary learning, and through utilizing a relationship between the wavelet subband and the K space, the traditional problem of image rebuilding in compressed sensing magnetic resonance is...