177 citations · 608 across the 34 of their papers we have counts for
7 papers · 1 filter
Sampling of graph signals via randomized local aggregations
Diego Valsesia, Giulia Fracastoro, Enrico Magli
Sampling of signals defined over the nodes of a graph is one of the crucial problems in graph signal processing. While in classical signal processing sampling is a well defined ope…
Fast and Lightweight Rate Control for Onboard Predictive Coding of Hyperspectral Images
Diego Valsesia, Enrico Magli
Predictive coding is attractive for compression of hyperspecral images onboard of spacecrafts in light of the excellent rate-distortion performance and low complexity of recent sch…
Graded quantization for multiple description coding of compressive measurements
Diego Valsesia, Giulio Coluccia, Enrico Magli
Compressed sensing (CS) is an emerging paradigm for acquisition of compressed representations of a sparse signal. Its low complexity is appealing for resource-constrained scenarios…
Compressive Signal Processing with Circulant Sensing Matrices
Diego Valsesia, Enrico Magli
Compressive sensing achieves effective dimensionality reduction of signals, under a sparsity constraint, by means of a small number of random measurements acquired through a sensin…
Joint recovery algorithms using difference of innovations for distributed compressed sensing
Diego Valsesia, Giulio Coluccia, Enrico Magli
Distributed compressed sensing is concerned with representing an ensemble of jointly sparse signals using as few linear measurements as possible. Two novel joint reconstruction alg…
Spatially Scalable Compressed Image Sensing with Hybrid Transform and Inter-layer Prediction Model
Diego Valsesia, Enrico Magli
Compressive imaging is an emerging application of compressed sensing, devoted to acquisition, encoding and reconstruction of images using random projections as measurements. In thi…
Graded Quantization: Democracy for Multiple Descriptions in Compressed Sensing
Diego Valsesia, Giulio Coluccia, Enrico Magli
The compressed sensing paradigm allows to efficiently represent sparse signals by means of their linear measurements. However, the problem of transmitting these measurements to a r…