Multi-attention feature fusion network for lightweight image super-resolution

Cong Liu; Dan Qu; Xu Yang; Nianwen Si

doi:10.1117/12.2634640

12 May 2022 Multi-attention feature fusion network for lightweight image super-resolution

Cong Liu, Dan Qu, Xu Yang, Nianwen Si

Proceedings Volume 12173, International Conference on Optics and Machine Vision (ICOMV 2022); 1217315 (2022) https://doi.org/10.1117/12.2634640
Event: International Conference on Optics and Machine Vision (ICOMV 2022), 2022, Guangzhou, China

Abstract

Aiming at the problems of image super-resolution reconstruction method based on convolutional neural network, such as complex structure, huge parameter amount, and slow reconstruction speed, this paper proposes a multi-attention feature fusion network for lightweight image super-resolution. The channel attention mechanism and pixel attention mechanism are used to fully extract image feature information and improve the feature extraction ability of the network. At the same time, the use of depthwise convolution effectively reduces the amount of network parameters and calculations. The experimental results show that under the premise that the reconstruction performance of the network is competitive, the network is lighter and the reconstruction speed is further improved.

Citation Download Citation

Cong Liu, Dan Qu, Xu Yang, and Nianwen Si "Multi-attention feature fusion network for lightweight image super-resolution", Proc. SPIE 12173, International Conference on Optics and Machine Vision (ICOMV 2022), 1217315 (12 May 2022); https://doi.org/10.1117/12.2634640

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