Results 121 to 130 of about 1,231,296 (158)

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
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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

3D Reconstruction with Sparse Image Sets

2011 Irish Machine Vision and Image Processing Conference, 2011
3D reconstruction with sparse image sets requires more accurate view geometry estimation than a large number of images based 3D reconstruction. In this paper, we have proposed an automatic 3D reconstruction system based on a small set of images which can estimate the view transformation between different views accurately.
Jiao Tian, Derek Molloy
openaire   +2 more sources

Bayesian learning for sparse signal reconstruction

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004
Sparse Bayesian learning and specifically relevance vector machines have received much attention as a means of achieving parsimonious representations of signals in the context of regression and classification. We provide a simplified derivation of this paradigm from a Bayesian evidence perspective and apply it to the problem of basis selection from ...
David P. Wipf, Bhaskar D. Rao
openaire   +2 more sources

Speech Reconstruction by Sparse Linear Prediction

2012
This paper proposes a new variant of the least square autoregressive (LSAR) method for speech reconstruction, which can estimate via least squares a segment of missing samples by applying the linear prediction (LP) model of speech. First, we show that the use of a single high-order linear predictor can provide better results than the classic LSAR ...
Ján Koloda, Antonio M. Peinado, Victoria E. Sánchez   +2 more
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

Learning Splines for Sparse Tomographic Reconstruction

2014
In a few-view or limited-angle computed tomography (CT), where the number of measurements is far fewer than image unknowns, the reconstruction task is an ill-posed problem. We present a spline-based sparse tomographic reconstruction algorithm where content-adaptive patch sparsity is integrated into the reconstruction process.
Elham Sakhaee, Alireza Entezari
openaire   +1 more source

Evolutionary algorithms for sparse signal reconstruction

Signal, Image and Video Processing, 2019
This study includes an evolutionary algorithm technique for sparse signal reconstruction in compressive sensing. In general, l1 minimization and greedy algorithms are used to reconstruct sparse signals. In addition to these methods, recently, heuristic algorithms have begun to be used to reconstruct sparse signals.
Murat Emre Erkoc, Nurhan Karaboga
openaire   +2 more sources