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Ghost imaging video algorithm based on the multidimensional vector matrix Walsh transform of bidirectional N-aligned

Shengqi Feng, Aijun Sang, Xiaoni Li

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    Summary

    This study introduces a novel ghost imaging video algorithm using bidirectional N-aligned fusion frames and deep learning. The method enhances image quality by reducing noise and motion blur, improving detail in reconstructed images.

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    Area of Science:

    • Computational imaging
    • Optical engineering
    • Digital signal processing

    Background:

    • Ghost imaging systems using multidimensional vector matrix Walsh transform sample moving objects with correlated frames.
    • Previous work overcame digital micromirror device refresh rate limitations, enabling more detailed frame reconstruction.

    Purpose of the Study:

    • To enhance ghost imaging video quality by improving single-frame detail and utilizing temporal-spatial correlations.
    • To propose a novel ghost imaging video algorithm for improved multi-frame quality.

    Main Methods:

    • A ghost imaging video algorithm based on bidirectional N-aligned fusion frames and multidimensional vector matrix Walsh transform.
    • Integration of deep learning with computational ghost imaging using a bidirectional N-alignment algorithm and a neural network framework.
    • Development of an encoding module and feature fusion module inspired by GoogleNet Inception V3, with a custom loss function for four-dimensional vector matrix Walsh transform ghost imaging.

    Main Results:

    • Significant improvements in structural similarity (18.58% increase), blur index (31.9% increase), and noise index (9.22% increase) compared to existing methods.
    • Enhanced ghost imaging videos with reduced noise, motion blur, and richer single-frame details.

    Conclusions:

    • The proposed algorithm effectively improves ghost imaging video quality by leveraging detailed frames and deep learning.
    • The method offers a significant advancement in reconstructing high-quality ghost imaging videos of moving objects.