Lightweight Image Denoising Network for Multimedia Teaching System

Xuanyu Zhang, Chunwei Tian (), Qi Zhang, Hong-Seng Gan, Tongtong Cheng and Mohd Asrul Hery Ibrahim
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Xuanyu Zhang: School of Software, Northwestern Polytechnical University, Xi’an 710129, China
Chunwei Tian: School of Software, Northwestern Polytechnical University, Xi’an 710129, China
Qi Zhang: School of Economics and Management, Harbin Institute of Technology at Weihai, Weihai 264209, China
Hong-Seng Gan: School of AI and Advanced Computing, XJTLU Entrepreneurship College (Taicang), Xi’an Jiaotong-Liverpool University, Suzhou 215400, China
Tongtong Cheng: School of Power and Energy, Northwestern Polytechnical University, Xi’an 710129, China
Mohd Asrul Hery Ibrahim: Faculty of Entrepreneurship and Business, Universiti Malaysia Kelantan, Kota Bharu 16100, Malaysia

Mathematics, 2023, vol. 11, issue 17, 1-11

Abstract: Due to COVID-19, online education has become an important tool for teachers to teach students. Also, teachers depend on a multimedia teaching system (platform) to finish online education. However, interacted images from a multimedia teaching system may suffer from noise. To address this issue, we propose a lightweight image denoising network (LIDNet) for multimedia teaching systems. A parallel network can be used to mine complementary information. To achieve an adaptive CNN, an omni-dimensional dynamic convolution fused into an upper network can automatically adjust parameters to achieve a robust CNN, according to different input noisy images. That also enlarges the difference in network architecture, which can improve the denoising effect. To refine obtained structural information, a serial network is set behind a parallel network. To extract more salient information, an adaptively parametric rectifier linear unit composed of an attention mechanism and a ReLU is used into LIDNet. Experiments show that our proposed method is effective in image denoising, which can also provide assistance for multimedia teaching systems.

Keywords: lightweight CNN; dynamic convolution; adaptive activation function; image denoising; multimedia teaching system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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