Name
Abstract | ||
|---|---|---|
Recently multiple high performance algorithms have been developed to infer high-resolution images from low-resolution image input using deep learning algorithms. The related problem of super-resolution from blurred or corrupted low-resolution images has however received much less attention. In this work, we propose a new deep learning approach that simultaneously addresses deblurring and super-resolution from blurred low resolution images. We evaluate the state-of-the-art super-resolution convolutional neural network (SR-CNN) architecture proposed in [1] for the blurred reconstruction scenario and propose a revised deeper architecture that proves its superiority experimentally both when the levels of blur are known and unknown a priori. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MLSP.2018.8516983 | 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | Field | DocType |
Image super-resolution,deblurring,deep learning,convolutional neural networks | Pattern recognition,Deblurring,Convolutional neural network,Computer science,A priori and a posteriori,Artificial intelligence,Deep learning,Superresolution | Conference |
ISSN | ISBN | Citations |
1551-2541 | 978-1-5386-5478-1 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fatma Albluwi | 1 | 0 | 0.68 |
| Vladimir A. Krylov | 2 | 133 | 14.81 |
| Rozenn Dahyot | 3 | 340 | 32.62 |