skip to main content
10.1145/3587716.3587773acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicmlcConference Proceedingsconference-collections
research-article

Super-Resolution Infrared Imaging via Degraded Information Distillation Network

Published: 07 September 2023 Publication History

Abstract

Infrared imaging technology is widely used in civil and military applications. However, the cost of making high-resolution infrared detectors is expensive. For this reason, we propose an unsupervised super-resolution infrared imaging method for degraded information distillation network. We design a network model for progressive extraction of degradation information to learn more degradation information with discriminative features. We use dual-attention convolution to achieve feature adaption in both channel and space. We use sub-pixel convolution to implement the reconstruction of infrared images. We train our model using infrared images and evaluate the proposed method systematically. The experimental results show that our proposed method has good performance compared to other state-of-the-art methods.

References

[1]
F. Liu, P. Han, Y. Wang, X. Li, L. Bai, X. Shao, Super resolution reconstruction of infrared images based on classified dictionary learning, Infrared Phys. Technol. 90 (2018) 146-155.
[2]
Z. He, S. Tang, J. Yang, Y. Cao, M.Y. Yang, Y. Cao, Cascaded deep networks with multiple receptive fields for infrared image super-resolution, IEEF Trans. Circuits Syst. Video Technol. 29 (8) (2019) 2310-2322.
[3]
L. Zhang and X. Wu. An edge-guided image interpolation algorithm via directional filtering and data fusion. TIP, 2006.
[4]
K. Zhang, X. Gao, D. Tao, and X. Li. Single image super-resolution with non-local means and steering kernel regression. TIP, 2012.
[5]
R. Timofte, V. De, and L. V. Gool. Anchored neighborhood regression for fast example-based super-resolution. In ICCV, 2013.
[6]
C. Dong, C. C. Loy, K. He, and X. Tang. Learning a deep convolutional network for image super-resolution. In ECCV, 2014.
[7]
J. Kim, J. Kwon Lee, and K. Mu Lee. Accurate image super-resolution using very deep convolutional networks. In CVPR, 2016.
[8]
J. Kim, J. Kwon Lee, and K. Mu Lee. Deeply-recursive convolutional network for image super-resolution. In CVPR, 2016.
[9]
B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee. Enhanced deep residual networks for single image super-resolution. In CVPRW, 2017
[10]
D. Huang, W. Huang, P. Gu, P. Liu, Y. Luo, Image super-resolution reconstruction based on regularization technique and guided filter, Infrared Phys. Technol. 83 (2017) 103-113.
[11]
Z. Wang, D. Liu, J Yang, W Han, T. Huang, Deep networks for image super-resolution with sparse prior, Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 370-378.
[12]
Y. Choi, N. Kim, S. Hwang, and I. So, “Thermal Image Enhancement using Convolutional Neural Network,” in Int. Conf. Intell. Robot. Syst., 2016.
[13]
Wang L, Wang Y, Dong X, Unsupervised Degradation Representation Learning for Blind Super-Resolution[J]. 2021.
[14]
Zhang L, Yang F, Ji L. Multi-scale fusion algorithm based on structure similarity index constraint for infrared polarization and intensity images. IEEE Access 2017; 5:24646–55.
[15]
Van Vliet L J, Young I T, Beckers G L. A nonlinear Laplace operator as edge detector in noisy images[J]. Computer Vision, Graphics, and Image Processing, 1989, 45(2): 167-195.
[16]
Zan G, Wei J, Kongfeng Z, Chao W. Automatic focusing algorithm based on Roberts' gradient[J]. Infrared and Laser Engineering.
[17]
Miao Q, Wang B. A novel adaptive multi-focus image fusion algorithm based on PCNN and sharpness[C]. Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense IV. SPIE, 2005, 5778: 704-712.
[18]
Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 1637-1645.
[19]
Hui Z, Wang X, Gao X. Fast and accurate single image super-resolution via information distillation network[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 723-731.
[20]
Li Z, Yang J, Liu Z, Feedback network for image super-resolution[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 3867-3876.

Index Terms

  1. Super-Resolution Infrared Imaging via Degraded Information Distillation Network
      Index terms have been assigned to the content through auto-classification.

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      ICMLC '23: Proceedings of the 2023 15th International Conference on Machine Learning and Computing
      February 2023
      619 pages
      ISBN:9781450398411
      DOI:10.1145/3587716
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 07 September 2023

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Information distillation
      2. Key words: Infrared imaging
      3. Super resolution
      4. Transformer

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Conference

      ICMLC 2023

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 29
        Total Downloads
      • Downloads (Last 12 months)9
      • Downloads (Last 6 weeks)1
      Reflects downloads up to 20 Jan 2025

      Other Metrics

      Citations

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media