Accelerated deep learning denoising for edge AI in single-pixel imaging

Abstract Single-pixel imaging (SPI) uses pixel arrays with coded illumination and a single detector to reconstruct an image of a scene. However, it is time-consuming due to the many projected patterns and non-trivial reconstruction. We pursue a practical route to real-time SPI by pairing fast linear reconstructions with a compact U-Net denoiser on an embedded GPU. In a fully simulated pipeline, we form undersampled reconstructions from CelebA-derived $$64\times 64$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>64</mml:mn> <mml:mo>×</mml:mo> <mml:mn>64</mml:mn> </mml:mrow> </mml:math> grayscale faces using Hadamard patterns ordered by Cake Cutting at $$M/N\in \{4,8,16,24,32\%\}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>M</mml:mi> <mml:mo>/</mml:mo> <mml:mi>N</mml:mi> <mml:mo>∈</mml:mo> <mml:mo>{</mml:mo> <mml:mn>4</mml:mn> <mml:mo>,</mml:mo> <mml:mn>8</mml:mn> <mml:mo>,</mml:mo> <mml:mn>16</mml:mn> <mml:mo>,</mml:mo> <mml:mn>24</mml:mn> <mml:mo>,</mml:mo> <mml:mn>32</mml:mn> <mml:mo>%</mml:mo> <mml:mo>}</mml:mo> </mml:mrow> </mml:math> . The denoiser is trained with mean squared error (MSE) on normalized images and deployed on a Jetson Orin NX 16 GB (FP32). We measure the acquisition time, reconstruction time, and inference time of the U-Net model per frame. Quality is reported with PSNR and SSIM, and performance with serial latency and pipelined throughput. Results indicate that GPU inference markedly cuts denoising time, shifting the bottleneck toward optical acquisition and/or the linear step as sampling grows. The study offers a reproducible recipe–data generation, models, and timing methodology–for assessing SPI denoising on edge hardware, and outlines levers to raise throughput: higher-rate pattern projection, optimized reconstruction kernels, and right-sized U-Net variants that preserve PSNR/SSIM while lowering latency.