[1]TIAN Chunwei,SONG Mingjian,ZUO Wangmeng,et al.Application of convolutional neural networks in image super-resolution[J].CAAI Transactions on Intelligent Systems,2025,20(3):719-749.[doi:10.11992/tis.202409027]
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Application of convolutional neural networks in image super-resolution

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 3 Page number: 719-749 Column: 人工智能校长论坛 Public date: 2025-05-05
Title:
Application of convolutional neural networks in image super-resolution
Author(s):
TIAN Chunwei1; 2;  SONG Mingjian3;  ZUO Wangmeng1;  DU Bo4;  ZHANG Yanning2; 5;  ZHANG Shichao6
1. Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China;
2. National Engineering Laboratory for Big Data Application Technology of Integrated Space, Air, Ground, and Sea, Xi’an 710192, China;
3. School of Software, Northwestern Polytechnical University, Xi’an 710072, China;
4. School of Computer Science, Wuhan University, Wuhan 430072, China;
5. School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China;
6. School of Computer Science and Engineering, Guangxi Normal University, Guilin 541004, China
Keywords:
deep learning; convolutional neural networks; image reconstruction; image processing; image restoration; image resolution; neural networks; low-level vision
CLC:
TP391.41
DOI:
10.11992/tis.202409027
Abstract:
Known for their strong learning abilities, convolutional neural networks (CNNs) have become mainstream methods for image super-resolution. However, substantial differences exist among deep learning methods of various types, and there is limited literature to summarize the relations and differences of different methods in image super-resolution. Thus, it is important to summarize such studies according to the loading capacity and the execution speed of devices. This paper first introduces the principles of CNNs in image super-resolution and then introduces CNN-based bicubic interpolation, nearest neighbor interpolation, bilinear interpolation, transposed convolution, subpixel layering, and meta-upsampling for image super-resolution to analyze the differences and relations of different CNN-based interpolations and modules. The performance of these methods is compared through experiments. Finally, this paper presents potential research points and drawbacks and summarizes the whole paper to promote the development of CNNs in image super-resolution.
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