We present a thorough implementation of the two-stage framework proposed in [A. Cicone, M. Huska, S.H. Kang and S. Morigi, JOT: a Variational Signal Decomposition into Jump, Oscillation and Trend, IEEE Transactions on Signal Processing, 2022]. The method assumes as input a 1D signal represented by a finite-dimensional vector in R^N. In the first stage the signal is decomposed into Jump (piece-wise constant), Oscillation, and Trend (smooth) components, and in the second stage the results are refined using residuals of other components. We propose an efficient numerical solution for the first st ..read more

Multi-stage progressive image restoration network (MPRNet) is a three-stage CNN (convolutional neural network) for image restoration. MPRNet has been shown to provide high performance gains on several datasets for a range of image restoration problems including image denoising, deblurring, and deraining. The network is interesting because it manages to remove the three kinds of artifacts with a single architecture. Here, we provide an overview of the network and study its performance and computational complexity in comparison with other state-of-the-art methods. **This is an MLBriefs article ..read more

The corresponding point coordinates determined by classic image matching approaches define local zero-order approximations of the global mapping between two images. But the patches around keypoints typically contain more information, which may be exploited to obtain a first-order approximation of the mapping, incorporating local affine maps between corresponding keypoints. Several methods have been proposed in the literature to compute this first-order approximation. In this paper we present several modifications of the RANSAC (RANdom SAmple Consensus) algorithm, that uses affine approximation ..read more

Federated Learning (FL) is a learning paradigm where multiple nodes collaboratively train a model by only exchanging updates or parameters. This enables to keep data locally, therefore enhancing privacy - statement requiring nuance, e.g. memorization of training data in language models. Depending on the application, the number of samples that each node contains can be very different, which can impact the training and the final performance. This work studies the impact of the per-node sample size distribution on the mean squared error (MSE) of the one-shot federated estimator. We focus on one-s ..read more

We compare five monocular depth estimation methods based on deep learning. This comparison focuses on how well methods generalize rather than a quantitative comparison on a specific dataset. This study shows that while monocular depth estimation methods work well on images similar to training images, they often show artifacts when applied on images out of the training distribution. We evaluate the different methods with images similar to training data and images with unusual point of views (e.g. top-down) or paintings. The readers are invited to judge by themselves about the advantages and dra ..read more

Binary shapes, or silhouettes, are building elements of logos, graphic symbols and fonts， which require various forms of geometric editing without compromising the resolution. In this paper, we present an effective silhouette vectorization algorithm that extracts the outline of a 2D shape from a raster binary image and converts it to a combination of cubic Bézier polygons and perfect circles. Compared to state-of-the-art image vectorization software, this algorithm has demonstrated a superior reduction in the number of control points while maintaining high accuracy ..read more

This paper details the first publicly available implementation of the progressive mesh compression algorithm described in the paper entitled "Compressed Progressive Meshes" [R. Pajarola and J. Rossignac, IEEE Transactions on Visualization and Computer Graphics, 6 (2000), pp. 79-93]. Our implementation is generic, modular, and includes several improvements in the stopping criteria and final encoding. Given an input 2-manifold triangle mesh, an iterative simplification is performed, involving batches of edge collapse operations guided by an error metric. During this compression step, all the inf ..read more

Time series of satellite images are now massively available thanks to the existence of several constellations of recurrent satellites. We propose a method for detecting and measuring the duration of changes on such series. This approach is intended to be generic and independent of the type of satellite used, whether band limited or multispectral. It is based on a global analysis of the sequence. The statistical detection method is applied to a residual sequence computed from backward and forward novelty filters applied to all images in the series. Significant changes are computed with a guaran ..read more