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