Infrared image super-resolution enhancement technology based on improved real-ESRGAN

Hao Chen; Yifan Yang; Ruijie Hao; Jing Xu; Yuexing Wang

doi:10.1117/12.3005252

21 December 2023 Infrared image super-resolution enhancement technology based on improved real-ESRGAN

Hao Chen, Yifan Yang, Ruijie Hao, Jing Xu, Yuexing Wang

Proceedings Volume 12966, AOPC 2023: AI in Optics and Photonics ; 129660B (2023) https://doi.org/10.1117/12.3005252
Event: Applied Optics and Photonics China 2023 (AOPC2023), 2023, Beijing, China

Abstract

Due to the defects such as low resolution, lack of hierarchy, and blurred visual effects in infrared images, the accuracy of detecting infrared images is much lower than that of detecting visible images. In this study, we propose a super-resolution enhancement method for single-frame infrared images based on improved Real-ESRGAN to resolve the problem of low resolution and lack of detailed texture of infrared images so that we can improve the accuracy of object detection. We add attention mechanism based on the original network to improve the network’s ability to extract the detailed texture. In addition, we also adjust the training epochs of the generator and discriminator to accelerate the generator update and prevent the discriminator from converging too fast. We also use pooling layers for downsampling to remain the important detailed texture features and make it easier for convolution layers to extract detailed features. The experimental results show that compared with original Real-ESRGAN, when improving the resolution twice as before, our improved network reach an increment of 6.23% of PSNR and 13.9% of SSIM, and when improving the resolution four times as before, the increment is 0.95% of PSNR and 4.42% of SSIM. In object detection, we use YOLOv5 to detect super-resolution infrared images generated by our improved network and the original infrared images and reach an increment of 2.95% of mAP. These promising results confirm that our network works effectively as a method of infrared image super-resolution enhancement and improves object detection accuracy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hao Chen, Yifan Yang, Ruijie Hao, Jing Xu, and Yuexing Wang "Infrared image super-resolution enhancement technology based on improved real-ESRGAN", Proc. SPIE 12966, AOPC 2023: AI in Optics and Photonics , 129660B (21 December 2023); https://doi.org/10.1117/12.3005252

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KEYWORDS

Infrared imaging

Infrared radiation

Super resolution

Image enhancement

Object detection

Image quality

Infrared detectors

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