用低成本光子雪崩二极管阵列+轻量模型，实现实时超分辨成像
On-Device Super Resolution Imaging Using Low-Cost SPAD Array and Embedded Lightweight Deep Learning
- 基于48x32分辨率的SPAD阵列，设计轻量级超分辨网络
- 在合成与真实数据上均实现256x256甚至512x512超分重建
- 嵌入Arduino UNO Q，支持实时视频流处理，适合边缘部署
本文提出一种面向消费级单光子雪崩二极管（SPAD）阵列的轻量级超分辨率（LiteSR）神经网络，该阵列空间分辨率为48x32。所提框架可重建256x256的高分辨率（HR）图像，并在合成与真实数据集上进行评估。定量指标显示在合成数据上重建保真度高，真实室内与室外测量进一步验证了方法的鲁棒性。此外，SPAD传感器连接至Arduino UNO Q微控制器，接收低分辨率（LR）深度与强度图像，输入压缩后的预训练深度学习模型，实现实时超分辨率视频流。除256x256外，还评估多种目标分辨率，确定在噪声污染的低分辨率输入下，最高可达512x512的超分效果。所提出的LiteSR嵌入式系统协同设计，为当前消费级SPAD阵列提供了一种可扩展、低成本的高分辨率成像解决方案。
原文摘要 · Abstract (English)
This work presents a lightweight super-resolution (LiteSR) neural network for depth and intensity images acquired from a consumer-grade single-photon avalanche diode (SPAD) array with a 48x32 spatial resolution. The proposed framework reconstructs high-resolution (HR) images of size 256x256. Both synthetic and real datasets are used for performance evaluation. Extensive quantitative metrics demonstrate high reconstruction fidelity on synthetic datasets, while experiments on real indoor and outdoor measurements further confirm the robustness of the proposed approach. Moreover, the SPAD sensor is interfaced with an Arduino UNO Q microcontroller, which receives low-resolution (LR) depth and intensity images and feeds them into a compressed, pre-trained deep learning (DL) model, enabling real-time SR video streaming. In addition to the 256x256 setting, a range of target HR resolutions is evaluated to determine the maximum achievable upscaling resolution (512x512) with LiteSR, including scenarios with noise-corrupted LR inputs. The proposed LiteSR-embedded system co-design provides a scalable, cost-effective solution to enhance the spatial resolution of current consumer-grade SPAD arrays to meet HR imaging requirements.
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