Residual attention network using multi-channel dense connections for image super-resolution

Zhiwei Liu; Ji Huang; Chengjia Zhu; Xiaoyu Peng; Xinyu Du

doi:10.1007/s10489-020-01723-2

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Residual attention network using multi-channel dense connections for image super-resolution

Zhiwei Liu Ji Huang Chengjia Zhu Xiaoyu Peng Xinyu Du

Year: 2020 Journal: Applied Intelligence Vol: 51 (1)Pages: 85-99 Publisher: Springer Science+Business Media

DOI: 10.1007/s10489-020-01723-2

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Keywords:

Computer science Discriminative model Residual Convolutional neural network Benchmark (surveying) Channel (broadcasting) Feature (linguistics) Artificial intelligence Pattern recognition (psychology) Representation (politics) Image (mathematics) Construct (python library) Attention network Algorithm Telecommunications Computer network

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Advanced Image Processing Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Image and Signal Denoising Methods

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Image Processing Techniques and Applications

Physical Sciences → Engineering → Media Technology

Residual attention network using multi-channel dense connections for image super-resolution

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