Results 151 to 160 of about 1,177,560 (190)

Wideband Sparse Reconstruction for Scanning Radar

IEEE Transactions on Geoscience and Remote Sensing, 2018
Recently, the generalized sparse iterative covariance-based estimation algorithm was extended to allow for varying norm constraints in scanning radar applications. In this paper, further to this development, we introduce a wideband dictionary framework which can provide a computationally efficient estimation of sparse signals.
Yongchao Zhang 0001, Andreas Jakobsson, Yin Zhang 0003, Yulin Huang 0001, Jianyu Yang 0001   +4 more
openaire   +1 more source

Sparse angle CT reconstruction based on group sparse representation

Journal of X-Ray Science and Technology, 2022
OBJECTIVE: In order to solve the problem of image quality degradation of CT reconstruction under sparse angle projection, we propose to develop and test a new sparse angle CT reconstruction method based on group sparse. METHODS: In this method, the group-based sparse representation is introduced into the statistical iterative reconstruction framework ...
Yanan, Gu, Yi, Liu, Wenting, Liu, Rongbiao, Yan, Yuhang, Liu, Zhiguo, Gui   +5 more
openaire   +2 more sources

A spline framework for sparse tomographic reconstruction

2013 IEEE 10th International Symposium on Biomedical Imaging, 2013
We present a spline-based sparse tomographic reconstruction framework. The proposed method utilizes the closed-form analytical Radon transform of B-splines and box splines of any order and integrates the (transform-domain) sparsity of the image into the reconstruction algorithm.
Mahsa Mirzargar, Elham Sakhaee, Alireza Entezari   +2 more
openaire   +1 more source

An Evolutionary Multiobjective Approach to Sparse Reconstruction

IEEE Transactions on Evolutionary Computation, 2014
This paper addresses the problem of finding sparse solutions to linear systems. Although this problem involves two competing cost function terms (measurement error and a sparsity-inducing term), previous approaches combine these into a single cost term and solve the problem using conventional numerical optimization methods.
Lin Li 0016, Xin Yao 0001, Rustam Stolkin, Maoguo Gong, Shan He 0001   +4 more
openaire   +2 more sources

Group sparse reconstruction for image segmentation

Neurocomputing, 2014
Abstract Image segmentation is a fundamental problem in computer vision and image analysis. Specially, the segmentation of medical images can assist doctors in making decisions. Due to the lack of distinctive features to describe the boundary of an organ and match function with high performance for features, medical image segmentation is difficult to
Xiaoqiang Lu, Xuelong Li 0001
openaire   +3 more sources

Sparse reconstruction of quantized speech signals

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
We propose sparse reconstruction techniques to improve the quality and/or reduce the bit-rate of standard speech coders. To that end, we assume signal sparsity in some transform domain and formulate the problem of reconstructing the original signal in terms of constrained l1-norm minimization.
Christoph Brauer, Timo Gerkmann, Dirk A. Lorenz   +2 more
openaire   +1 more source

Image compression via sparse reconstruction

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
The 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 0002, Oscar C. Au, Amin Zheng, Haitao Yang 0001, Ketan Tang, Wenxiu Sun   +5 more
openaire   +2 more sources

Jointly Sparse Reconstructed Regression Learning

2018
Least 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 0001, Heng Kong
openaire   +2 more sources

A robust reconstruction of sparse biomagnetic sources

IEEE Transactions on Biomedical Engineering, 1997
Inequalty constraints are introduced to a normalized minimum-L1-norm estimator, which gives a sparse solution of the biomagnetic inverse problem. The constraints have a numeric tolerance to take into account the measurement ambiguity caused by noise. Computer simulation and phantom-data analysis show how the solution is improved by the constraints with
Kentaro Matsuura, Yoichi Okabe
openaire   +2 more sources

Overlapping blocks in reconstruction of sparse images

2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2017
Images are commonly analysed by the discrete cosine transform (DCT) on a number of blocks of smaller size. The blocks are then combined back to the original size image. Since the DCT of blocks have a few nonzero coefficients, the images can be considered as sparse in this transformation domain.
Isidora Stankovic, Milos Dakovic, Irena Orovic   +2 more
openaire   +2 more sources