Jeremias Sulam

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Over the last decade, a number of algorithms have shown promising results in removing additive white Gaussian noise from natural images, and though different, they all share in common a patch based strategy by locally denoising overlapping patches. While this lowers the complexity of the problem, it also causes noticeable artifacts when dealing with large(More)
Sparse representation has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance. Combined with the ability to learn a dictionary from signal examples, sparsity-inspired algorithms are often achieving state-of-the-art results in a wide variety of tasks. These methods have(More)
—The celebrated sparse representation model has led to remarkable results in various signal processing tasks in the last decade. However, despite its initial purpose of serving as a global prior for entire signals, it has been commonly used for modeling low dimensional patches due to the computational constraints it entails when deployed with learned(More)
—The convolutional sparse model has recently gained increasing attention in the signal and image processing communities , and several methods have been proposed for solving the pursuit problem emerging from it – in particular its convex relaxation, Basis Pursuit. In the first of this two-part work, we have provided a theoretical backbone for this model,(More)
—Most state-of-the-art denoising algorithms employ a patch-based approach by enforcing a local model or prior, such as self similarity, sparse representation, or Gaussian Mixture Model (GMM). While applying these models, these algorithms implicitly build a notion of similarity between the image pixels. This can be formulated as an image-adaptive(More)
Convolutional Sparse Coding (CSC) is an increasingly popular model in the signal and image processing communities, tackling some of the limitations of traditional patch-based sparse representations. Although several works have addressed the dictionary learning problem under this model, these relied on an ADMM formulation in the Fourier domain, losing the(More)
Image inpainting is concerned with the completion of missing data in an image. When the area to inpaint is relatively large, this problem becomes challenging. In these cases, traditional methods based on patch models and image propagation are limited, since they fail to consider a global perspective of the problem. In this letter, we employ a recently(More)
The problem of system classification consists of identifying the source system corresponding to a certain output signal. In the context of dynamical systems, the outputs are usually given in the form of time series, and this identification process includes determining the underlying states of the system or their intrinsic set of parameters. In this work we(More)
In ultrasound, second harmonic imaging is usually preferred due to the higher clutter artifacts and speckle noise common in the first harmonic image. Typical ultrasound use either one or the other image, applying corresponding filters for each case. In this work we propose a method based on a joint sparsity model that fuses the first and second harmonic(More)
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