Special Issue "Dictionary Learning Algorithms and Applications"
Sparse representations have found numerous applications in signal and image processing, coding, compression, classification, modeling and other fields. Their success relies on the parsimony principle: few members of an overcomplete basis can offer a large variety of models. The overcomplete basis, or dictionary, can be fixed or adapted to the application.
Dictionary learning is the technique of designing dictionaries based on samples from the process to be modeled. In many applications, learned dictionaries offer better performance than fixed ones. There are already well established algorithms for the standard problem, but the topic is still open for variations of the learning problem and especially for applications.
Images may be corrupted by salt and pepper impulse noise during image acquisitions or transmissions. Although promising denoising performances have been recently obtained with sparse representations, how to restore high-quality images remains challenging and open. In this work, image sparsity is enhanced with [...] Read more.
Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., [...] Read more.
Abstract: Control problems involving autonomous vehicles often require a priori knowledge about existing features (obstacles, restricted or target areas, etc.). Therefore, the description of the environment is of paramount importance for the subsequent motion planning strategies. We propose the approximation of the regions of interest through a collection of zonotopic sets whose center and scaling are the result of dictionary learning procedure whose goal is to minimize the complexity of the representation while also providing a close approximation of the initial representation.