"Sparse Signal Processing." New Perspectives on Approximation and Sampling Theory. Springer International Publishing, 2014. 189-213.M. Azghani and F. Marvasti, "Sparse signal processing," in New Perspec- tives on Approximation and Sampling Theory, pp. 189-213, Springer, 2014....
Sparse Representation for Brain Signal Processing: A tutorial on methods and applications In many cases, observed brain signals can be assumed as the linear mixtures of unknown brain sources/components. It is the task of blind source separation ... Yuanqing,Li,Zhu,... - 《Signal Processing Maga...
Chambers, "A unified approach to sparse signal processing." EURASIP Journal on Advances in Signal Processing, vol. 2012, p. 44, 2012.Marvasti, F, Amini, A, Haddadi, F, Soltanolkotabi, M, Khalaj, BH, Aldroubi, A, Sanei, S, Chambers, J (2012) A unified approach to sparse signal ...
SparseImageandSignalProcessing sparse image and signal processing: wavelets, curvelets, morphological diversity (pdf) by jean luc starck (ebook)This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ...
This survey of the basic features of most topics of sparse signal processing is an invaluable resource for researchers and graduate students in applied mathematics and signal processing. Without doubt, this work will stimulate the further research. 展开 关键词: Signal processing ...
Theodoridis, "Online sparse system identification and signal reconstruction using projections onto weighted balls," IEEE Transactions on Signal Processing, vol. ... Y Kopsinis,K Slavakis,S Theodoridis - 《IEEE Transactions on Signal Processing》 ...
machine-learningalgorithmssignal-processingsparse-codingdictionary-learning UpdatedApr 28, 2017 MATLAB Point cloud completion tool based on dictionary learning. Takes a PCL point cloud surface and fills in gaps or densifies sparse regions by learning from the various surface features of the cloud. This...
signal processing, compression and learning problems. In this talk, we present our multi-scale sparse convolutional learning framework for large scale point cloud processing, with applications to the geometry and attributes super-resolution, and ...
We treat the phase retrieval (PR) problem of reconstructing the interest signal from its Fourier magnitude. Since the Fourier phase information is lost, the problem is ill-posed. Several techniques have been used to address this problem by utilizing vari
image inpainting via sparse representation. in ieee international conference on acoustics, speech and signal processing (icassp 2009) . piscataway: ieee; 2009:697-700. chapter google scholar xu z, sun j: image inpainting by patch propagation using patch sparsity. ieee trans. image ...