Interferenceless coded aperture correlation holography (I-COACH), which utilizes spatial incoherent light illumination to achieve interferenceless and non-scanning three-dimensional (3D) imaging, has revolutionized the field of incoherent holography. However, existing correlation reconstruction algorithms often suffer from significant inter-plane crosstalk from multiple cross-sections of the sample, resulting in poor quality in 3D imaging. Here, we proposed a single-shot 3D reconstruction method of I-COACH via a Wiener deconvolution network combining total variation kernel (TV-K) functional constraint (WienerNet3D/TV-K). The method first performs multiple pairing of Wiener deconvolution and a convolutional neural network (CNN) and then uses the point spread hologram (PSH) library of the I-COACH system and object hologram (OH) as inputs, updating throughout the entire training process to learn the optimal filter and noise regularization parameters. Moreover, a TV-K function constraint is introduced to achieve more high-frequency details of the sample. Both simulation analysis and experimental results demonstrate that the proposed method has excellent performance in suppressing inter-plane crosstalk and significantly improves the signal-to-noise ratio in I-COACH 3D reconstruction. Importantly, this WienerNet3D/TV-K method will provide a useful strategy for the application of I-COACH dynamic 3D imaging in fluorescence microscopy, astronomy and other fields.

中文翻译：

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通过维纳反卷积/TV-K 约束实现无干扰编码孔径相关全息单次 3D 重建的平面间串扰抑制

无干扰编码孔径相关全息术（I-COACH）利用空间非相干光照明实现无干扰、非扫描三维（3D）成像，彻底改变了非相干全息领域。然而，现有的相关重建算法经常遭受来自样本多个横截面的显着的平面间串扰，导致 3D 成像质量较差。在这里，我们提出了一种通过维纳反卷积网络结合全变分核（TV-K）功能约束的 I-COACH 的单次 3D 重建方法（WienerNet3D/TV-K）。该方法首先执行维纳反卷积和卷积神经网络（CNN）的多重配对，然后使用 I-COACH 系统的点扩展全息图（PSH）库和物体全息图（OH）作为输入，在整个训练过程中进行更新学习最佳滤波器和噪声正则化参数。此外，引入TV-K函数约束以获得样本的更多高频细节。仿真分析和实验结果表明，该方法在抑制平面间串扰方面具有优异的性能，并且显着提高了I-COACH 3D重建的信噪比。重要的是，这种WienerNet3D/TV-K方法将为I-COACH动态3D成像在荧光显微镜、天文学等领域的应用提供有用的策略。