Marko Filipovic

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We propose a method for signal recovery in compressed sensing when measurements can be highly corrupted. It is based on &#x2113;<sub>p</sub> minimization for 0 &lt;; p &#x2264; 1. Since it was shown that &#x2113;<sub>p</sub> minimization performs better than &#x2113;<sub>1</sub> minimization when there are no large errors, the proposed approach is a natural(More)
Sparse representation of natural images over redundant dictionary enables solution of the inpainting problem. A major challenge, in this regard, is learning of a dictionary that is well adapted to the image. Efficient methods are developed for grayscale images represented in patch space by using, for example, K-SVD or independent component analysis(More)
Several supervised feature extraction methods for tensor objects have been proposed recently, with applications in recognition of objects, faces and handwritten digits. However, the existing methods usually use only second order statistics of the data, typically through calculation of the withinand between-class scatters. Here we propose a method for(More)
Bioinformatics data analysis is often using linear mixture model representing samples as additive mixture of components. Properly constrained blind matrix factorization methods extract those components using mixture samples only. However, automatic selection of extracted components to be retained for classification analysis remains an open issue. The method(More)
Nonlinear underdetermined blind separation of nonnegative dependent sources consists in decomposing a set of observed nonlinearly mixed signals into a greater number of original nonnegative and dependent component (source) signals. This hard problem is practically relevant for contemporary metabolic profiling of biological samples, where sources (a.k.a.(More)
Failure recovery in Flexible manufacturing systems is an active field of research. Some recovery strategies and concepts are presented but there is still no exact method and algorithm to apply in a real industrial application. Formal description of possible recovery trajectory types in means of a Petri net construction led research activities in one major(More)
Image inpainting consists in recovering missing parts of an image. Since a color image is a D array, tensor completion methods are applicable to this problem. Tensor completion approach based on trace norm minimization can be useful when the fraction of missing pixels is not large, with the advantage that the training set is not required. Here, we(More)
The first contribution of this paper is the comparison of learned dictionary based approaches to inpainting and denoising of images in natural scenes, where emphasis is given on the use of complete and overcomplete dictionary learned by independent component analysis. The second contribution of the paper relates to the formulation of a problem of denoising(More)
A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of(More)
We address the problem of restoration of images which have been affected by impulse or a combination of impulse and Gaussian noise. We propose a patch-based approach that exploits approximate sparse representations of image patches in learned dictionaries. For every patch, sparse representation in a dictionary is enforced by &#x2113;<sub>1</sub>-norm(More)
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