Learned spinning mask for high-speed single-pixel imaging

Single pixel imaging (SPI) holds significant promise for addressing specialized imaging challenges, including unconventional wavelength-ranges, proposed scattering scenarios, and low light conditions Recent developments in SPI employing a spinning mask have successfully insured the limits posed by traditional modulators like the Digital Micromirror Device (DMD), specifically with respect to refresh rates and operational spectral bands Never less, current spinning mask implementations, releasing on random patterns or cyclic Hadamard patterns, fall short in achieving rapid and high quality imaging when operating at low sampling rates In this investment, we propose to use deep learning to join optimize a coding scheme based on spinning mask for SPI On the encoding side, a cyclic mask, optimized by the convolutional layer, is meticulously crafted to modify the input object On the coding side, the object image is reconstructed from the modulated intensity fluctuations employing a lightweight neural network integrated with physical model By adapting this approach, we realized residual image results compiling 71x73 pixel images with a sampling rate of 4%, all while maintaining module rate of 2.4MHz. Not only, we have achieved image recording speeds exceeding 12KHz The proposed method dramatically improves the imaging effectiveness of SPI, there catalyzing the practical utilization of SPI in domains such as specialized wavelength imaging and high speed imaging 

单像素成像（Single Pixel Imaging, SPI）在解决非常规波段成像、预设散射场景以及低光照条件等专业成像难题方面展现出巨大应用潜力。近年来，采用旋转掩膜的单像素成像技术已成功突破了数字微镜器件（Digital Micromirror Device, DMD）这类传统调制器所设定的性能限制，具体涉及刷新率与工作光谱波段两大维度。然而，当前的旋转掩膜方案无论是生成随机图案还是循环哈达玛（Hadamard）图案，在低采样率下均难以实现快速且高质量的成像。本研究中，我们提出将深度学习与基于旋转掩膜的单像素成像编码方案进行联合优化。在编码端，我们精心设计了经卷积层优化得到的循环掩膜，以对输入目标进行调制；在解码环节，借助融合物理模型的轻量级神经网络，可从调制后的强度波动中重建目标图像。通过该方法，我们实现了采样率仅为4%的71×73像素图像的残差重建，同时仍可保持2.4MHz的调制速率。不仅如此，我们还实现了超过12kHz的图像录制速度。所提方法大幅提升了单像素成像的成像性能，从而推动了单像素成像在非常规波段成像与高速成像等领域的实际应用。

搜集汇总

背景与挑战

背景概述

该数据集聚焦于高速单像素成像技术，通过深度学习优化旋转掩模的循环编码方案，以提升成像速度和效率。它实现了71x73像素图像在低采样率下的高质量重建，模块速率达2.4MHz，成像速度超过12KHz，适用于特殊波长和高速成像场景。