Journal
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
Volume 8, Issue 7, Pages 1271-1280Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2021.1004009
Keywords
Convolutional neural network (CNN); lightweight framework; multi-scale; super-resolution
Categories
Funding
- National Natural Science Foundation of China [61772149, 61866009, 61762028, U1701267, 61702169]
- Guangxi Science and Technology Project [2019GXNSFFA245014, ZY20198016, AD18281079, AD18216004]
- Natural Science Foundation of Hunan Province [2020JJ3014]
- Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics [GIIP202001]
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In this paper, a fast and lightweight framework named weighted multi-scale residual network (WMRN) is proposed for a better tradeoff between image super-resolution performance and computational efficiency. The network utilizes depthwise separable convolutions and weighted multi-scale residual blocks to improve efficiency and multi-scale representation capability, with Convolutional layers in the reconstruction subnetwork to filter feature maps for high-quality image reconstruction. Extensive experiments show the effectiveness of WMRN compared to several state-of-the-art algorithms.
The tradeoff between efficiency and model size of the convolutional neural network (CNN) is an essential issue for applications of CNN-based algorithms to diverse real-world tasks. Although deep learning-based methods have achieved significant improvements in image super-resolution (SR), current CNN-based techniques mainly contain massive parameters and a high computational complexity, limiting their practical applications. In this paper, we present a fast and lightweight framework, named weighted multi-scale residual network (WMRN), for a better tradeoff between SR performance and computational efficiency. With the modified residual structure, depthwise separable convolutions (DS Convs) are employed to improve convolutional operations' efficiency. Furthermore, several weighted multi-scale residual blocks (WMRBs) are stacked to enhance the multi-scale representation capability. In the reconstruction subnetwork, a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality image. Extensive experiments were conducted to evaluate the proposed model, and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN.
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