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    Area of Science:

    • Computational imaging
    • Signal processing
    • Machine learning

    Background:

    • Single-pixel imaging (SPI) is a computational imaging technique that reconstructs images from measurements acquired by a single-pixel detector.
    • Traditional SPI reconstruction methods struggle with low sampling rates and high noise levels, leading to degraded image quality.
    • Existing deep learning approaches like U-Net and GANs have shown promise but can be further optimized for efficiency and robustness.

    Purpose of the Study:

    • To develop an efficient and robust framework for single-pixel imaging (SPI) reconstruction.
    • To leverage the unique properties of Kolmogorov-Arnold Networks (KANs) for improved nonlinear mapping in SPI.
    • To enhance image reconstruction quality under challenging conditions such as extremely low sampling and strong noise.

    Main Methods:

    • Proposed U-KAN-SPI, a U-shaped encoder-decoder framework integrating KAN structures.
    • Utilized KANwise modules in the encoder to model illumination pattern and bucket signal interactions while suppressing noise.
    • Incorporated KAN-enhanced skip connections for preserving fine structural details.
    • Implemented a lightweight attention module in the decoder for adaptive channel and spatial attention to improve robustness.

    Main Results:

    • U-KAN-SPI demonstrated superior performance compared to U-Net, attention-augmented variants, and GAN-based networks.
    • Achieved higher Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values.
    • Significantly reduced artifacts and produced sharper edges in reconstructed images.
    • Showcased excellent generalization capabilities across diverse scenes, even under extreme low-data and low-Signal-to-Noise Ratio (SNR) conditions.

    Conclusions:

    • KAN structures offer an efficient module for enhancing SPI reconstruction, particularly in low-data and low-SNR regimes.
    • The U-KAN-SPI framework provides a robust and effective solution for challenging SPI scenarios.
    • This work highlights the potential of KANs in advancing computational imaging techniques.