Compressive single-pixel imaging via a wavelength-multiplexed spatially incoherent diffractive optical processor
🤖 AI Summary
This work proposes an efficient compressive single-pixel imaging method that overcomes the limitations of low measurement efficiency and long acquisition times. By co-designing a data-agnostic static wavelength-multiplexed diffractive optical processor with a shallow neural network, the framework spectrally encodes the target using multi-wavelength LED illumination and captures the total optical power per spectral band via a single-pixel detector. A jointly trained shallow network then enables rapid image reconstruction. The approach eliminates the need for real-time modulation or complex hardware control while offering learnable point-spread-function modeling and end-to-end optimization. Numerical simulations and experiments using an LED array demonstrate its promising applicability in biomedical imaging, autonomous driving, and remote sensing.
Application Category
📝 Abstract
Despite offering high sensitivity, a high signal-to-noise ratio, and a broad spectral range, single-pixel imaging (SPI) is limited by low measurement efficiency and long data-acquisition times. To address this, we propose a wavelength-multiplexed, spatially incoherent diffractive optical processor combined with a compact/shallow digital artificial neural network (ANN) to implement compressive SPI. Specifically, we model the bucket detection process in conventional SPI as a linear intensity transformation with spatially and spectrally varying point-spread functions. This transformation matrix is treated as a learnable parameter and jointly optimized with a shallow digital ANN composed of 2 hidden nonlinear layers. The wavelength-multiplexed diffractive processor is then configured via data-free optimization to approximate this pre-trained transformation matrix; after this optimization, the diffractive processor remains static/fixed. Upon multi-wavelength illumination and diffractive modulation, the target spatial information of the input object is spectrally encoded. A single-pixel detector captures the output spectral power at each illumination band, which is then rapidly decoded by the jointly trained digital ANN to reconstruct the input image. In addition to our numerical analyses demonstrating the feasibility of this approach, we experimentally validated its proof-of-concept using an array of light-emitting diodes (LEDs). Overall, this work demonstrates a computational imaging framework for compressive SPI that can be useful in applications such as biomedical imaging, autonomous devices, and remote sensing.
Problem
Research questions and friction points this paper is trying to address.
single-pixel imaging
measurement efficiency
data-acquisition time
compressive imaging
computational imaging
Innovation
Methods, ideas, or system contributions that make the work stand out.
compressive single-pixel imaging
wavelength-multiplexed diffractive optics
spatially incoherent illumination
shallow neural network
computational imaging
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