Low signal-to-noise ratio (SNR) measurements, primarily due to the quartic attenuation of intensity with distance, are arguably the fundamental barrier to real-time, high-resolution, non-line-of-sight (NLoS) imaging at long standoffs. To better model, characterize, and exploit these low SNR measurements, we use spectral estimation theory to derive a noise model for NLoS correlography. We use this model to develop a speckle correlation-based technique for recovering occluded objects from indirect reflections. Then, using only synthetic data sampled from the proposed noise model, and without knowledge of the experimental scenes nor their geometry, we train a deep convolutional neural network to solve the noisy phase retrieval problem associated with correlography. We validate that the resulting deep-inverse correlography approach is exceptionally robust to noise, far exceeding the capabilities of existing NLoS systems both in terms of spatial resolution achieved and in terms of total capture time. We use the proposed technique to demonstrate NLoS imaging with 300 µm resolution at a 1 m standoff, using just two 1/8th ${s}$ exposure-length images from a standard complementary metal oxide semiconductor detector.

中文翻译：

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深度反相关成像：走向实时高分辨率非视距成像

低信噪比（SNR）测量（主要是由于强度随距离的四次衰减）可以说是长期进行实时，高分辨率，非视距（NLoS）成像的基本障碍僵局。为了更好地建模，表征和利用这些低SNR测量，我们使用频谱估计理论来推导NLoS相关成像的噪声模型。我们使用该模型来开发基于斑点相关性的技术，以从间接反射中恢复被遮挡的对象。然后，仅使用从建议的噪声模型中采样的合成数据，并且在不了解实验场景或其几何结构的情况下，我们训练了一个深度卷积神经网络来解决与相关成像相关的嘈杂相位检索问题。我们验证结果深度反相关成像方法具有出色的抗噪能力，无论在空间分辨率方面还是在总捕获时间方面都远远超过了现有NLoS系统的功能。我们仅使用来自标准互补金属氧化物半导体检测器的两张1 / 8th $ {s} $曝光长度图像，使用提出的技术来演示在1 m距离处具有300 µm分辨率的NLoS成像。