Results 1 to 10 of about 97,682 (166)
Application of Sparse Representation in Bioinformatics [PDF]
Frontiers in Genetics, 2021 Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method and displays powerful superiority.
Shuguang Han +8 more
doaj +2 more sources
A Hybrid Sparse Representation Model for Image Restoration [PDF]
Sensors, 2022 Group-based sparse representation (GSR) uses image nonlocal self-similarity (NSS) prior to grouping similar image patches, and then performs sparse representation.
Caiyue Zhou +5 more
doaj +2 more sources
A Survey of Sparse Representation: Algorithms and Applications
IEEE Access, 2015 Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision, and pattern recognition.
Zheng Zhang +4 more
doaj +3 more sources
OPTIMIZATION OF THE SPARSE REPRESENTATION PARAMETERS FOR THE FUSION OF REMOTELY SENSED SATELLITE IMAGES [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 Image fusion methods are widely used in remote sensing applications to obtain more information about the features in the study area. One of the recent satellite image fusion techniques that can deal with noise and reduce computational cost and deal with ...
A. Asefpour Vakilian, M. R. Saradjian
doaj +1 more source
Collaborative and Low-Rank Graph for Discriminant Analysis of Hyperspectral Imagery
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Sparse representation can be used for the representation of high-dimensional data into a low-dimensional subspace. Recently, sparse graph-based discriminant analysis that uses l1-norm optimization has drawn much attention in dimensionality reduction of ...
Chiranjibi Shah, Qian Du
doaj +1 more source
Sparse linear representation [PDF]
2009 IEEE International Symposium on Information Theory, 2009 5 pages, to appear in proc.
Halyun Jeong, Young-Han Kim 0001
openaire +2 more sources
Face Recognition Based on Robust Principal Component Analysis and Kernel Sparse Representation [PDF]
Jisuanji gongcheng, 2016 Aiming at the problems that the existing face recognition methods are hard to efficiently overcome the effect of noise and error disturbance (such as illumination,occlusion,and face expression).Kernel sparse representation classification based on Robust ...
LIAO Ruihua,LI Yongfan,LIU Hong
doaj +1 more source
Quantization of Sparse Representations [PDF]
2007 Data Compression Conference (DCC'07), 2007 Compressive sensing (CS) is a new signal acquisition technique for sparse and compressible signals. Rather than uniformly sampling the signal, CS computes inner products with randomized basis functions; the signal is then recovered by a convex optimization. Random CS measurements are universal in the sense that the same acquisition system is sufficient
Petros Boufounos, Richard G. Baraniuk
openaire +1 more source
Generating Images with Sparse Representations
CoRR, 2021 The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models. Previous approaches such as VQ-VAE use deep autoencoders to obtain compact representations, which are more practical as inputs for likelihood-based models.
Charlie Nash +3 more
openaire +3 more sources
Learning Sparse Representations of Depth [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2011 This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, typically obtained by laser range scanners or ...
Ivana Tosic +2 more
openaire +2 more sources