Super-Resolution Reconstruction of Motor Long-Wave Infrared Images Based on Improved USR-Net
Temperature analysis of long-wave infrared images serves as an effective approach for motor fault detection. However, current long-wave infrared cameras suffer from relatively low imaging resolution. To overcome this limitation, this paper proposes a super-resolution reconstruction method that empha...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10843668/ |
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| author | Darong Zhu Ziyan Sun Fangbin Wang |
| author_facet | Darong Zhu Ziyan Sun Fangbin Wang |
| author_sort | Darong Zhu |
| collection | DOAJ |
| description | Temperature analysis of long-wave infrared images serves as an effective approach for motor fault detection. However, current long-wave infrared cameras suffer from relatively low imaging resolution. To overcome this limitation, this paper proposes a super-resolution reconstruction method that emphasizes the restoration of complex edge structures in motor images. The method first applies adaptive Wiener filtering to eliminate environmental noise and subsequently incorporates a spatial attention mechanism (SA) within the residual blocks of the USR-Net to enhance edge feature extraction. An improved adaptive channel attention mechanism (Simple-Se) is introduced after the decoder to further refine the reconstruction of complex edges. The reconstruction performance is further optimized by combining perceptual loss and mean squared error loss. Experimental results indicate that, for 2x degraded images, the proposed method achieves a Peak Signal-to-Noise Ratio (PSNR) value exceeding 41 dB, outperforming other methods, with the Structural Similarity Index (SSIM) reaching 0.9872. Furthermore, the Learned Perceptual Image Patch Similarity (LPIPS) value for 2x degraded images is below 0.095. Overall, the proposed method demonstrates outstanding performance in image reconstruction quality, detail preservation, and visual effects, making it highly suitable for high-precision image restoration in practical applications requiring superior image quality. |
| format | Article |
| id | doaj-art-cbce2d4f7ebe4b78b35f5a2e52d17a17 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-cbce2d4f7ebe4b78b35f5a2e52d17a172025-01-28T00:01:13ZengIEEEIEEE Access2169-35362025-01-0113155561557110.1109/ACCESS.2025.353075910843668Super-Resolution Reconstruction of Motor Long-Wave Infrared Images Based on Improved USR-NetDarong Zhu0https://orcid.org/0000-0002-4632-2318Ziyan Sun1https://orcid.org/0009-0003-5260-4586Fangbin Wang2https://orcid.org/0000-0003-2565-8864School of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei, ChinaSchool of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei, ChinaSchool of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei, ChinaTemperature analysis of long-wave infrared images serves as an effective approach for motor fault detection. However, current long-wave infrared cameras suffer from relatively low imaging resolution. To overcome this limitation, this paper proposes a super-resolution reconstruction method that emphasizes the restoration of complex edge structures in motor images. The method first applies adaptive Wiener filtering to eliminate environmental noise and subsequently incorporates a spatial attention mechanism (SA) within the residual blocks of the USR-Net to enhance edge feature extraction. An improved adaptive channel attention mechanism (Simple-Se) is introduced after the decoder to further refine the reconstruction of complex edges. The reconstruction performance is further optimized by combining perceptual loss and mean squared error loss. Experimental results indicate that, for 2x degraded images, the proposed method achieves a Peak Signal-to-Noise Ratio (PSNR) value exceeding 41 dB, outperforming other methods, with the Structural Similarity Index (SSIM) reaching 0.9872. Furthermore, the Learned Perceptual Image Patch Similarity (LPIPS) value for 2x degraded images is below 0.095. Overall, the proposed method demonstrates outstanding performance in image reconstruction quality, detail preservation, and visual effects, making it highly suitable for high-precision image restoration in practical applications requiring superior image quality.https://ieeexplore.ieee.org/document/10843668/Long-wave infrared imagesuper-resolution reconstructionUSR-Netmotor |
| spellingShingle | Darong Zhu Ziyan Sun Fangbin Wang Super-Resolution Reconstruction of Motor Long-Wave Infrared Images Based on Improved USR-Net IEEE Access Long-wave infrared image super-resolution reconstruction USR-Net motor |
| title | Super-Resolution Reconstruction of Motor Long-Wave Infrared Images Based on Improved USR-Net |
| title_full | Super-Resolution Reconstruction of Motor Long-Wave Infrared Images Based on Improved USR-Net |
| title_fullStr | Super-Resolution Reconstruction of Motor Long-Wave Infrared Images Based on Improved USR-Net |
| title_full_unstemmed | Super-Resolution Reconstruction of Motor Long-Wave Infrared Images Based on Improved USR-Net |
| title_short | Super-Resolution Reconstruction of Motor Long-Wave Infrared Images Based on Improved USR-Net |
| title_sort | super resolution reconstruction of motor long wave infrared images based on improved usr net |
| topic | Long-wave infrared image super-resolution reconstruction USR-Net motor |
| url | https://ieeexplore.ieee.org/document/10843668/ |
| work_keys_str_mv | AT darongzhu superresolutionreconstructionofmotorlongwaveinfraredimagesbasedonimprovedusrnet AT ziyansun superresolutionreconstructionofmotorlongwaveinfraredimagesbasedonimprovedusrnet AT fangbinwang superresolutionreconstructionofmotorlongwaveinfraredimagesbasedonimprovedusrnet |