Name
Abstract | ||
|---|---|---|
Image deblurring in photon-limited conditions is ubiquitous in a variety of low-light applications such as photography, microscopy and astronomy. However, the presence of the photon shot noise due to the low illumination and/or short exposure makes the deblurring task substantially more challenging than the conventional deblurring problems. In this paper, we present an algorithm unrolling approach for the photon-limited deblurring problem by unrolling a Plug-and-Play algorithm for a fixed number of iterations. By introducing a three-operator splitting formation of the Plug-and-Play framework, we obtain a series of differentiable steps which allows the fixed iteration unrolled network to be trained end-to-end. The proposed algorithm demonstrates significantly better image recovery compared to existing state-of-the-art deblurring approaches. We also present a new photon-limited deblurring dataset for evaluating the performance of algorithms. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCI.2022.3209939 | IEEE Transactions on Computational Imaging |
Keywords | DocType | Volume |
Photon limited,poisson deconvolution,deblurring,plug-and-play,algorithm unrolling | Journal | 8 |
ISSN | Citations | PageRank |
2573-0436 | 0 | 0.34 |
References | Authors | |
31 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yash Sanghvi | 1 | 0 | 0.34 |
| Abhiram Gnanasambandam | 2 | 0 | 0.34 |
| Stanley H. Chan | 3 | 403 | 30.95 |