The amount of electronic data available today as well as its dimensionality and complexity increases rapidly in many scientific areas including biology, (bio-)chemistry, medicine, physics and its application fields like robotics, bioinformatics or multimedia technologies. Many of these data sets are very complex but have also a simple inherent structure which allows an appropriate sparse repres...

Sparse Representation (SR) shows powerful discriminating power when the training samples are sufficient to construct an overcomplete dictionary. However, in the lack of training samples case, the dictionary is too small to sparsely represent the test sample which restricts the classification performance of sparse representation. In order to address this problem, we propose a new sparse represen...

This paper suggests a nonparametric scheme to find the sparse solution of the underdetermined system of linear equations in the presence of unknown impulsive or nonGaussian noise. This approach is robust against any variations of the noise model and its parameters. It is based on minimization of rank pseudo norm of the residual signal and of the signal of interest, simultaneously. We use the st...

This is the first report on Working Paper WP-RFM-14-01. The potential and capability of sparse representations is well-known. However, their (multivariate variable) vectorial form, which is completely fine in many fields and disciplines, results in removal and filtering of important “spatial” relations that are implicitly carried by two-dimensional [or multi-dimensional] objects, such as images...

Magnetic Resonance Imaging (MRI) is a noninvasive imaging method widely used in medical diagnosis. Data in MRI are obtained line-by-line within the K-space, where there are usually a great number of such lines. For this reason, magnetic resonance imaging is slow. MRI can be accelerated through several methods such as parallel imaging and compressed sensing, where a fraction of the K-space lines...

Ordinary images, as well as most natural and manmade signals, are compressible and can, therefore, be well represented in a domain in which the signal is sparse. Sparse signal representations have found use in a large number of applications including image compression. Inspired by recent theoretical advances in sparse representation, we propose an image compression using wavelet, sparse represe...

Sparse representation has become very popular in fields of signal processing, image processing computer vision and pattern recognition. Sparse representation also has good reputation in both theoretical and practical applications. Images can be sparsely coded by structural primitives and recently the sparse coding or sparse representation has been widely used to resolve the problems in image re...