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Albluwi, Fatma, Vladimir, A. Krylov and Dahyot, Rozenn (2019) Denoising RENOIR Image Dataset with DBSR. IMVIP 2019: Irish Machine Vision and Image Processing.
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Abstract
Noise reduction algorithms have often been evaluated using images degraded by artificially synthesised
noise. The RENOIR image dataset [3] provides an alternative way for testing noise reduction algorithms
on real noisy images and we propose in this paper to assess our CNN called De-Blurring Super-Resolution
(DBSR) [2] to reduce the natural noise due to low light conditions in a RENOIR dataset.
| Item Type: | Article |
|---|---|
| Keywords: | image denoising; super-reolution; RENOIR dataset; DBSR; |
| Academic Unit: | Faculty of Science and Engineering > Computer Science |
| Item ID: | 15156 |
| Identification Number: | 10.21427/g34k-8r27 |
| Depositing User: | Rozenn Dahyot |
| Date Deposited: | 20 Dec 2021 12:45 |
| Journal or Publication Title: | IMVIP 2019: Irish Machine Vision and Image Processing |
| Publisher: | Arrow @ TU Dublin |
| Refereed: | Yes |
| Related URLs: | |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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