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| Edge Chip Deployment Methods for Lightweight Infrared Computational Imaging Reconstruction Algorithms |
| ZHAO Ziyu1，2， WANG Xuquan1，2， MA Jie3， XING Yujie1，2， DUN Xiong1，2， WANG Zhanshan1，2， CHENG Xinbin1，2 |
| 1. Institute of Precision Optical Engineering， School of Physics Science and Engineering， Tongji University， Shanghai 200092， China； 2. MOE Key Laboratory of Advanced Micro-Structured Materials， Tongji University， Shanghai 200092， China； 3. College of Electronics and Information Engineering， Tongji University， Shanghai 200092， China |
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Abstract By integrating intelligent algorithm-driven image processing techniques, computational imaging has the potential to transcend the limits of conventional hardware-centric optical systems, enabling optical systems to achieve high performance and a compact design. Focusing on the image reconstruction requirements in lightweight infrared single-lens computational imaging, this study investigated lightweight model deployment methodologies tailored for edge AI chips. Through targeted operator optimisation, model pruning, and quantisation implemented on edge devices, the deployed U-Net reconstruction model achieved a 52.3% reduction in parameters and a 60.3% reduction in computational operations, resulting in a 56% acceleration in edge processing frame rate while sacrificing only 0.91 dB in PSNR and 0.021 in SSIM. Further architectural simplification allowed ultra-high-speed video-rate on-chip image reconstruction exceeding 95 FPS, at the cost of just 1.3 dB PSNR and 0.018 SSIM. The experiments examined edge hardware acceleration for computational single-chip infrared camera reconstruction algorithms. This study provides technical references for engineering applications of lightweight infrared computational imaging systems.
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Received: 11 February 2025
Published: 10 September 2025
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2025, Vol. 8 
