2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-5.1
Paper Title HOCA: HIGHER-ORDER CHANNEL ATTENTION FOR SINGLE IMAGE SUPER-RESOLUTION
Authors Yalei Lv, Tao Dai, Bin Chen, Tsinghua University, China; Jian Lu, Shenzhen University, China; Shu-Tao Xia, Tsinghua University, China; Jingchao Cao, City University of Hong Kong, China
SessionIVMSP-5: Super-resolution 1
LocationGather.Town
Session Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVTEC] Image & Video Processing Techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Convolutional neural networks (CNNs) have obtained great success in single image super-resolution (SR). More recent works (e.g., RCAN and SAN) have obtained remarkable performance with channel attention based on first- or second-order statistics of features. However, these methods neglect the rich feature statistics higher than second-order, thus hindering the representation ability of CNNs. To address this issue, we propose a higher-order channel attention (HOCA) module to enhance the representation ability of CNNs. In our HOCA module, to capture different types of semantic information, we first compute k-order of feature statistics, followed by channel attention to learn the feature interdependencies. Considering the diversity of input contents, we design a gate mechanism to adaptively select a specific k-order channel attention. Besides, our HOCA module serves as a plug-and-play module and can be easily plugged into existing state-of-art CNN-based SR methods. Extensive experiments on public benchmarks show that our HOCA module effectively improves the performance of various CNN-based SR methods.