Name
Abstract | ||
|---|---|---|
We present an algorithm to directly restore a clear highresolution image from a blurry low-resolution input. This problem is highly ill-posed and the basic assumptions for existing super-resolution methods (requiring clear input) and deblurring methods (requiring high-resolution input) no longer hold. We focus on face and text images and adopt a generative adversarial network (GAN) to learn a category-specific prior to solve this problem. However, the basic GAN formulation does not generate realistic high-resolution images. In this work, we introduce novel training losses that help recover fine details. We also present a multi-class GAN that can process multi-class image restoration tasks, i.e., face and text images, using a single generator network. Extensive experiments demonstrate that our method performs favorably against the state-of-the-art methods on both synthetic and real-world images at a lower computational cost. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICCV.2017.36 | 2017 IEEE International Conference on Computer Vision (ICCV) |
Keywords | Field | DocType |
text images,generative adversarial network,high-resolution images,multiclass image restoration tasks,blurry low-resolution input,super-resolution methods,GAN formulation | Iterative reconstruction,Kernel (linear algebra),Computer vision,Generative adversarial network,Pattern recognition,Deblurring,Computer science,Artificial intelligence,Image restoration,Image resolution | Conference |
Volume | Issue | ISSN |
2017 | 1 | 1550-5499 |
ISBN | Citations | PageRank |
978-1-5386-1033-6 | 20 | 0.63 |
References | Authors | |
40 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangyu Xu | 1 | 143 | 5.66 |
| Deqing Sun | 2 | 1061 | 44.84 |
| Jin-shan Pan | 3 | 567 | 30.84 |
| Yu Jin Zhang | 4 | 1272 | 93.14 |
| Hanspeter Pfister | 5 | 5933 | 340.59 |
| Yang Ming-Hsuan | 6 | 15303 | 620.69 |