Abstract
Variational-based methods are the state-of-the-art in sparse
image deconvolution. Yet, this class of methods might not scale to large
dimensions of interest in current high resolution imaging applications.
To overcome this limitation, we propose to solve the sparse deconvolution
problem through a two-step approach consisting in first solving
(approximately and fast) an optimization problem following by a neural
network for ”Deep Post Processing” (DPP). We illustrate our method in
radio astronomy, where algorithms scalability is paramount due to the
extreme data dimensions. First results suggest that DPP is able to achieve
similar quality to state-of-the-art methods in a fraction of the time.
image deconvolution. Yet, this class of methods might not scale to large
dimensions of interest in current high resolution imaging applications.
To overcome this limitation, we propose to solve the sparse deconvolution
problem through a two-step approach consisting in first solving
(approximately and fast) an optimization problem following by a neural
network for ”Deep Post Processing” (DPP). We illustrate our method in
radio astronomy, where algorithms scalability is paramount due to the
extreme data dimensions. First results suggest that DPP is able to achieve
similar quality to state-of-the-art methods in a fraction of the time.
| Original language | English |
|---|---|
| Number of pages | 2 |
| Publication status | Published - 1 Jul 2019 |
| Event | Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop - Toulouse, France Duration: 1 Jul 2019 → 4 Jul 2019 |
Workshop
| Workshop | Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop |
|---|---|
| Abbreviated title | SPARS 2019 |
| Country/Territory | France |
| City | Toulouse |
| Period | 1/07/19 → 4/07/19 |