Name
Abstract | ||
|---|---|---|
Recently, extensive studies on a generative adversarial network (GAN) have made great progress in single image super-resolution (SISR). However, there still exists a significant difference between the reconstructed high-frequency and the real high-frequency details. To address this issue, this study presents an SISR approach based on conditional GAN (SRCGAN). SRCGAN includes a generator network that generates super-resolution (SR) images and a discriminator network that is trained to distinguish the SR images from ground-truth high-resolution (HR) ones. Specifically, the discriminator network uses the ground-truth HR image as a conditional variable, which guides the network to distinguish the real images from the SR images, facilitating training a more stable generator model than GAN without this guidance. Furthermore, a residual-learning module is introduced into the generator network to solve the issue of detail information loss in SR images. Finally, the network is trained in an end-to-end manner by optimizing a perceptual loss function. Extensive evaluations on four benchmark datasets including Set5, Set14, BSD100, and Urban100 demonstrate the superiority of the proposed SRCGAN over state-of-the-art methods in terms of PSNR, SSIM, and visual effect. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1049/iet-ipr.2018.6570 | Iet Image Processing |
Keywords | Field | DocType |
learning (artificial intelligence),image reconstruction,image resolution | Generative adversarial network,Pattern recognition,Artificial intelligence,Superresolution,Mathematics | Journal |
Volume | Issue | ISSN |
13 | 14 | 1751-9659 |
Citations | PageRank | References |
1 | 0.39 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaojiao Qiao | 1 | 2 | 0.73 |
| Huihui Song | 2 | 18 | 3.68 |
| Kaihua Zhang | 3 | 1591 | 56.35 |
| Xiaolu Zhang | 4 | 2 | 0.73 |
| QingShan Liu | 5 | 2625 | 162.58 |