190 citations · 1.2k across the 44 of their papers we have counts for
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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…
A Parallel Compressive Imaging Architecture for One-Shot Acquisition
Tomas Björklund, Enrico Magli
A limitation of many compressive imaging architectures lies in the sequential nature of the sensing process, which leads to long sensing times. In this paper we present a novel arc…
Smoothness-Constrained Image Recovery from Block-Based Random Projections
Giulio Coluccia, Diego Valsesia, Enrico Magli
In this paper we address the problem of visual quality of images reconstructed from block-wise random projections. Independent reconstruction of the blocks can severely affect visu…
Progressive Compressed Sensing and Reconstruction of Multidimensional Signals Using Hybrid Transform/Prediction Sparsity Model
Giulio Coluccia, Simeon Kamden-Kuiteng, Andrea Abrardo +2
Compressed sensing (CS) is an innovative technique allowing to represent signals through a small number of their linear projections. Hence, CS can be thought of as a natural candid…
A Novel Progressive Image Scanning and Reconstruction Scheme based on Compressed Sensing and Linear Prediction
Giulio Coluccia, Enrico Magli
Compressed sensing (CS) is an innovative technique allowing to represent signals through a small number of their linear projections. In this paper we address the application of CS…
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…