Feature distillation network based on enhanced full-channel attention for lightweight image super-resolution

Quanyin Li; Rui Xiang; Yan Huang; Guoyou Wang

doi:10.1117/12.2681037

8 June 2023 Feature distillation network based on enhanced full-channel attention for lightweight image super-resolution

Quanyin Li, Rui Xiang, Yan Huang, Guoyou Wang

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Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127071L (2023) https://doi.org/10.1117/12.2681037
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

In recent years, deep learning-based methods have made a big splash in the field of single image super-resolution (SISR), and a lot of excellent research works on lightweight image super-resolution networks have emerged. This series of approaches point a way forward for lightweight SR network design, but they generally suffer from two problems: 1) slow convergence of the network 2) poor image reconstruction quality. In this paper, we firstly propose a simple and effective learning rate decay scheme without additional cost, and the new strategy can accelerate the convergence speed of the network, and the model obtained in the same training time is about 0.05 dB higher in PSNR test results. Secondly, we think about the performance bottleneck of RFDN and propose an enhanced full-channel attention (EFA) module with a mixture of channel and spatial attention, which can improve about 0.085 dB in PSNR compared to the CCA attention module. Finally, we propose a new feature distillation network based on the EFA module, called EFAFDN. With only 523k parameters, the EFAFDN(x2) is lighter than the RFDN with 534k parameters and has better SR performance than the RFDN-L with 626k parameters. Extensive experimental results show that EFAFDN performs slightly better than RLFN, the winning model of the main track of the NTIRE 2022 Efficient Super-Resolution Challenge.

Citation Download Citation

Quanyin Li, Rui Xiang, Yan Huang, and Guoyou Wang "Feature distillation network based on enhanced full-channel attention for lightweight image super-resolution", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127071L (8 June 2023); https://doi.org/10.1117/12.2681037

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KEYWORDS

Performance modeling

Image enhancement

Super resolution

RGB color model

Data modeling

Image restoration

Convolution

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