Tomer Peleg

Tomer Peleg
Technion - Israel Institute of Technology | technion · Electrical Engineering Group

M.Sc.

About

8
Publications
1,177
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1,020
Citations

Publications

Publications (8)
Article
We address single image super-resolution using a statistical prediction model based on sparse representations of low- and high-resolution image patches. The suggested model allows us to avoid any invariance assumption, which is a common practice in sparsity-based approaches treating this task. Prediction of high resolution patches is obtained via M...
Article
The synthesis-based sparse representation model for signals has drawn considerable interest in the past decade. Such a model assumes that the signal of interest can be decomposed as a linear combination of a few atoms from a given dictionary. In this paper we concentrate on an alternative, analysis-based model, where an analysis operator-hereafter...
Article
Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in which the signal is assumed to be generated as a combination of a few atoms from a given dictionary. In this work we consider a Bayesian setting and go beyond the classi...
Conference Paper
The synthesis-based sparse representation model for signals has drawn a considerable interest in the past decade. Such a model assumes that the signal of interest can be decomposed as a linear combination of a few atoms from a given dictionary. In this paper we concentrate on an alternative, analysis-based model, where an Analysis Dictionary multip...
Article
Full-text available
The co-sparse analysis model for signals assumes that the signal of interest can be multiplied by an analysis dictionary \Omega, leading to a sparse outcome. This model stands as an interesting alternative to the more classical synthesis based sparse representation model. In this work we propose a theoretical study of the performance guarantee of t...
Conference Paper
We address the problem of denoising for image patches. The approach taken is based on Bayesian modeling of sparse representations, which takes into account dependencies between the dictionary atoms. Following recent work, we use a Boltzman machine to model the sparsity pattern. In this work we focus on the special case of a unitary dictionary and o...
Article
We revisit the problem of recovering a continuous-time signal lying within a known shift-invariant subspace from nonlinear and nonideal samples. Recently, an iterative algorithm for perfect recovery of such signals was proposed. This method requires operations which are not linear time-invariant (LTI), rendering it impractical due to its polynomial...
Conference Paper
We address the problem of motion blur removal from an image sequence that was acquired by a sensor with nonlinear response. Motion blur removal in purely linear settings has been studied extensively in the past. In practice however, sensors exhibit nonlinearities, which also need to be compensated for. In this paper we study the problem of joint mo...

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