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Challenges in Sparse Image Reconstruction
International Journal of Image and Graphics, 2020Handling huge amount of data from different sources more so in the images is the latest challenge. One of the solutions to this is sparse representation. The idea of sparsity has been receiving much attention recently from many researchers in the areas such as satellite image processing, signal processing, medical image processing, microscopy image ...
S. Shashi Kiran, K. V. Suresh
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Image compression via sparse reconstruction
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014The traditional compression system only considers the statistical redundancy of images. Recent compression works exploit the visual redundancy of images to further improve the coding efficiency. However, the existing works only provide suboptimal visual redundancy removal schemes. In this paper, we propose an efficient image compression scheme based on
Yuan Yuan +5 more
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Fourier reconstruction with sparse inversion
Geophysical Prospecting, 2007ABSTRACTThe problem of seismic data reconstruction is posed as an inverse problem where the objective is to obtain the Fourier coefficients that synthesize the signal. Once the coefficients have been found, they are used to reconstruct the data on a uniformly spaced grid.
P. Zwartjes, A. Gisolf
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Sparse reconstruction of ISOMAP representations
Journal of Intelligent & Fuzzy Systems, 2019Isometric feature mapping (ISOMAP) is one of the classical methods of nonlinear dimensionality reduction (NLDR) and seeks for low dimensional (LD) structure of high dimensional (HD) data. However, the inverse problem of ISOMAP has never been investigated, which recovers the HD sample from the related LD sample, and its application prospect in data ...
Li, Honggui, Trocan, Maria
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DOA Estimation for Sparse Array via Sparse Signal Reconstruction
IEEE Transactions on Aerospace and Electronic Systems, 2013The problem of direction-of-arrival (DOA) estimation for sparse array is addressed. The perspective that DOA estimation in virtual array response model can be cast as the problem of sparse recovery is introduced. Two methods are proposed, based on different optimization problems, which are solvable using second-order cone (SOC) programming. Without the
Nan Hu, Zhongfu Ye, Xu Xu, Ming Bao
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Sparse modelling and sparse signal reconstruction
Abstract Signal models are central to solving inverse problems, and reconstruction methods either implicitly or explicitly make use of signal models. Assuming the unknown signal of interest lies in a class of signals described by a signal model, we wish to reconstruct the signal with an algorithm that is sample efficient (i.e., only ...openaire +1 more source
Fourier diffusion for sparse CT reconstruction
Medical Imaging 2024: Physics of Medical ImagingSparse CT reconstruction continues to be an area of interest in a number of novel imaging systems. Many different approaches have been tried including model-based methods, compressed sensing approaches, and most recently deep-learning-based processing.
Anqi, Liu +2 more
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Jointly Sparse Reconstructed Regression Learning
2018Least squares regression and ridge regression are simple and effective methods for feature selection and classification and many methods based on them are proposed. However, most of these methods have small-class problem, which means that the number of the projection learned by these methods is limited by the number of class.
Dongmei Mo, Zhihui Lai, Heng Kong
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Sparse MRI and CT Reconstruction
2017Sparse signal reconstruction is of the utmost importance for efficient medical imaging, conducting accurate screening for security and inspection, and for non-destructive testing. The sparsity of the signal is dictated by either feasibility, or the cost and the screening time constraints of the system.
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