A Light-Weight Self-Supervised Infrared Image Perception Enhancement Method
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| Publicat a: | Electronics vol. 13, no. 18 (2024), p. 3695 |
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| Altres autors: | , |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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|---|---|---|---|
| 001 | 3110458933 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics13183695 |2 doi | |
| 035 | |a 3110458933 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Xiao, Yifan |u National Laboratory of Automatic Target Recognition, National University of Defense Technology, Changsha 410073, China; <email>xiaoyifan.xyf@gmail.com</email>; College of Chemistry and Chemical Engineering, Xi’an Shiyou University, Xi’an 710065, China; <email>lizhouli@xsyu.edu.cn</email> | |
| 245 | 1 | |a A Light-Weight Self-Supervised Infrared Image Perception Enhancement Method | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Convolutional Neural Networks (<inline-formula>CNNs</inline-formula>) have achieved remarkable results in the field of infrared image enhancement. However, the research on the visual perception mechanism and the objective evaluation indicators for enhanced infrared images is still not in-depth enough. To make the subjective and objective evaluation more consistent, this paper uses a perceptual metric to evaluate the enhancement effect of infrared images. The perceptual metric mimics the early conversion process of the human visual system and uses the normalized Laplacian pyramid distance (<inline-formula>NLPD</inline-formula>) between the enhanced image and the original scene radiance to evaluate the image enhancement effect. Based on this, this paper designs an infrared image-enhancement algorithm that is more conducive to human visual perception. The algorithm uses a lightweight Fully Convolutional Network (<inline-formula>FCN</inline-formula>), with <inline-formula>NLPD</inline-formula> as the similarity measure, and trains the network in a self-supervised manner by minimizing the <inline-formula>NLPD</inline-formula> between the enhanced image and the original scene radiance to achieve infrared image enhancement. The experimental results show that the infrared image enhancement method in this paper outperforms existing methods in terms of visual perception quality, and due to the use of a lightweight network, it is also the fastest enhancement method currently. | |
| 653 | |a Radiance | ||
| 653 | |a Deep learning | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Visual perception | ||
| 653 | |a Image enhancement | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Visual fields | ||
| 653 | |a Design | ||
| 653 | |a Infrared imagery | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Image quality | ||
| 653 | |a Visual effects | ||
| 653 | |a Radiation | ||
| 653 | |a Visual perception driven algorithms | ||
| 700 | 1 | |a Zhang, Zhilong |u National Laboratory of Automatic Target Recognition, National University of Defense Technology, Changsha 410073, China; <email>xiaoyifan.xyf@gmail.com</email> | |
| 700 | 1 | |a Li, Zhouli |u College of Chemistry and Chemical Engineering, Xi’an Shiyou University, Xi’an 710065, China; <email>lizhouli@xsyu.edu.cn</email> | |
| 773 | 0 | |t Electronics |g vol. 13, no. 18 (2024), p. 3695 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3110458933/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3110458933/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3110458933/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |