Corpus ID: 237292756

Efficient Transformer for Single Image Super-Resolution

@article{Lu2021EfficientTF,
  title={Efficient Transformer for Single Image Super-Resolution},
  author={Zhisheng Lu and Hong Liu and Juncheng Li and Linlin Zhang},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.11084}
}
  • Zhisheng Lu, Hong Liu, +1 author Linlin Zhang
  • Published 2021
  • Computer Science
  • ArXiv
Single image super-resolution task has witnessed the great strides with the development of deep learning. However, most existing studies focus on building a more complex neural network with a massive number of layers, bringing heavy computational cost and memory storage. Recently, as Transformer yields brilliant results in NLP tasks, more and more researchers start to explore the application of Transformer in computer vision tasks. But with the heavy computational cost and high GPU memory… Expand
2 Citations
From Beginner to Master: A Survey for Deep Learning-based Single-Image Super-Resolution
TLDR
This survey gives an overview of DL-based SISR methods and group them according to their targets, such as reconstruction efficiency, reconstruction accuracy, and perceptual accuracy, as well as introducing some related works, including benchmark datasets, upsampling methods, optimization objectives, and image quality assessment methods. Expand
Transformers in Vision: A Survey
TLDR
This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline with an introduction to fundamental concepts behind the success of Transformers i.e., self-attention, large-scale pre-training, and bidirectional feature encoding. Expand

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