Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution

@article{Guo2020ClosedLoopMD,
  title={Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution},
  author={Yong Guo and Jian Chen and J. Wang and Qi Chen and Jiezhang Cao and Zeshuai Deng and Yanwu Xu and Mingkui Tan},
  journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={5406-5415}
}
  • Yong Guo, Jian Chen, +5 authors Mingkui Tan
  • Published 2020
  • Computer Science
  • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Deep neural networks have exhibited promising performance in image super-resolution (SR) by learning a nonlinear mapping function from low-resolution (LR) images to high-resolution (HR) images. However, there are two underlying limitations to existing SR methods. First, learning the mapping function from LR to HR images is typically an ill-posed problem, because there exist infinite HR images that can be downsampled to the same LR image. As a result, the space of the possible functions can be… Expand
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