Photon Limited Non-Blind Deblurring Using Algorithm Unrolling
| Autor: | Yash Sanghvi, Abhiram Gnanasambandam, Stanley H. Chan |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Computational Mathematics
Computer Science::Computer Vision and Pattern Recognition Image and Video Processing (eess.IV) Signal Processing FOS: Electrical engineering electronic engineering information engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Electrical Engineering and Systems Science - Image and Video Processing Computer Science Applications |
| Zdroj: | IEEE Transactions on Computational Imaging. 8:851-864 |
| ISSN: | 2334-0118 2573-0436 |
| DOI: | 10.1109/tci.2022.3209939 |
| Popis: | 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 photon shot noise due to 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. IEEE Transactions on Computational Imaging |
| Databáze: | OpenAIRE |
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