The Electrocardiogram (ECG) signal is one of the recognizing approaches to discover heart disease. One of the major difficulties in biomedical data processing like electrocardiography is the separation of the original signal from noises affected by body movement and respiration, electromagnetic field, power line and high frequency interference. Various methods of digital filters are exploited t...

Directional multiscale representations such as shearlets and curvelets have gained increasing recognition in recent years as superior methods for the sparse representation of data. Thanks to their ability to sparsely encode images and other multidimensional data, transformdomain denoising algorithms based on these representations are among the best performing methods currently available. As alr...

cDNA micro arrays are more and more frequently used in molecular biology as they can give insight into the relation of an organism's metabolism and its genome. The process of imaging a micro array sample can introduce a great deal of noise and bias into the data with higher variance than the original signal which may swamp the useful information. As imperfections and fabrication artifacts often...

Designing an efficient over-complete dictionary is an important issue for developing a learning based system of super-resolution. To obtain fast solution, the size of dictionary needs to be reduced. However it may lower the performance as dictionary maybe incomplete. To address this issue, in this paper, we propose an improvement of dictionary learning for image super-resolution based on sparse...

In allusion to the losses of image detail and texture structure information during image de-noising process, an image de-noising algorithm based on non-related dictionary learning is proposed in this paper. Firstly, this algorithm is adopted to obtain selfadaption redundant dictionary for the noisy image through the dictionary learning algorithm; secondly, HOG features and gray-level statistica...

This paper develops column partition based distributed schemes for a class of convex sparse optimization problems, e.g., basis pursuit (BP), LASSO, denosing (BPDN), and their extensions, fused LASSO. We are particularly interested in the cases where number (scalar) decision variables is much larger than measurements, each agent has limited memory or computing capacity such that it only knows sm...

Phase imaging and wavefront reconstruction from noisy observations of complex exponent is a topic of this paper. It is a highly non-linear problem because the exponent is a 2π-periodic function of phase. The reconstruction of phase and amplitude is difficult. Even with an additive Gaussian noise in observations distributions of noisy components in phase and amplitude are signal dependent and no...

Among various kinds of image denoising methods, the Perona–Malik model is a representative Partial Differential Equation based (PDE-based) algorithm which effectively removes the noise as well as having edge enhancement simultaneously through anisotropic diffusion controlled by the diffusion coefficient. However, the unstable behavior of the Perona–Malik model introduces staircasing artifacts i...