1. Conditional generative adversarial network with densely-connected residual learning for single image super-resolution
- Author
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Huihui Song, Jiaojiao Qiao, Zhang Xiaolu, and Kaihua Zhang
- Subjects
Visual perception ,Computer Networks and Communications ,Computer science ,business.industry ,020207 software engineering ,Pattern recognition ,02 engineering and technology ,Residual ,Superresolution ,Hardware and Architecture ,0202 electrical engineering, electronic engineering, information engineering ,Media Technology ,Leverage (statistics) ,Artificial intelligence ,Single image ,business ,Generative adversarial network ,Software - Abstract
Recently, generative adversarial network (GAN) has been widely employed in single image super-resolution (SISR), achieving favorably good perceptual effects. However, the SR outputs generated by GAN still have some fictitious details, which are quite different from the ground-truth images, resulting in a low PSNR value. In this paper, we leverage the ground-truth high-resolution (HR) image as a useful guide to learn an effective conditional GAN (CGAN) for SISR. Among it, we design the generator network via residual learning, which introduces dense connections to the residual blocks to effectively fuse low and high-level features across different layers. Extensive evaluations show that our proposed SR method performs much better than state-of-the-art methods in terms of PSNR, SSIM, and visual perception.
- Published
- 2020