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In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version,...
Sparsity-inducing penalties are useful tools in variational methods for machine learning. In this paper, we propose two block-coordinate descent strategies for learning a sparse multiclass support vector machine. The first one works by selecting a subset of features to be updated at each iteration, while the second one performs the selection among the training samples. These algorithms can be efficiently...
We propose a proximal approach to deal with a class of convex variational problems involving nonlinear constraints. A large family of constraints, proven to be effective in the solution of inverse problems, can be expressed as the lower-level set of a sum of convex functions evaluated over different blocks of the linearly transformed signal. For such constraints, the associated projection operator...
The Parallel Proximal Algorithm (PPXA+) has been recently introduced as an efficient tool for solving convex optimization problems. It has proved particularly effective in the context of stereo vision, used as the methodological core of a novel disparity estimation technique. In this work, the main methodological issues limiting the efficient parallelization of this technique are addressed, and further...
The concept of cosparsity has been recently introduced in the arena of compressed sensing. In cosparse modelling, the ℓ0 (or ℓ1) cost of an analysis-based representation of the target signal isminimized under a data fidelity constraint. By taking benefit from recent advances in proximal algorithms, we show that it is possible to efficiently address a more general framework where a convex block sparsity...
The Photo-Response Non-Uniformity (PRNU) has been recently introduced [1, 2] as a powerful tool to detect image forgeries. In spite of its effectiveness in many scenarios, the proposed method fails to detect small manipulations. In this work we propose a modified version of the detection algorithm described in [2], based on a preliminary segmentation of the image, which guarantees a better detection...
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