前向散射光子编码信号检索的漫射光场成像模型
A Diffuse Light Field Imaging Model for Forward-Scattering Photon-Coded Signal Retrieval
AI总结：
针对超强散射环境下的低信噪比成像难题，提出漫射光场成像模型（DLIM），利用光场成像检索前向散射光子作为信号，基于扩散方程恢复弹道光子源项，实验证明在极低信噪比下仍能重建目标。
摘要：
散射成像常常因散射噪声的普遍存在而受到极低信噪比（SNRs）的阻碍。光场成像已被证明能有效抑制噪声并收集更多弹道光子作为信号。然而，为了克服超强散射环境中的信噪比极限，即使采用光场框架，仅有的稀有弹道信号也是不足的。受辐射传输理论的启发，我们提出了一种漫射光场成像模型（DLIM），该模型利用光场成像检索前向散射光子作为信号，以克服超强散射环境引起的低信噪比成像挑战。该模型旨在基于扩散方程，从前向散射光子中恢复弹道光子信号作为源项。DLIM 包含两个主要过程：辐射度建模和漫射光场近似。辐射度建模使用提出的三平面参数化分析散射光场图像中的辐射度分布，该参数化求解一个描述散射光场脉冲函数的四维辐射核。然后，散射光场图像合成一个满足控制前向散射光子的扩散方程的漫射源，该方程在成像空间的诺伊曼边界条件下求解。这是第一个物理感知的散射光场成像模型，将传统光场成像框架从自由空间扩展到漫射空间。大量实验证实，即使散射光场图像在极低信噪比下退化为随机噪声，DLIM 也能重建目标物体。
英文摘要：
Scattering imaging is often hindered by extremely low signal-to-noise ratios (SNRs) due to the prevalence of scattering noise. Light field imaging has been shown to be effective in suppressing noise and collect more ballistic photons as signals. However, to overcome the SNR limit in super-strong scattering environments, even with light field framework, only rare ballistic signals are insufficient. Inspired by radiative transfer theory, we propose a diffuse light field imaging model (DLIM) that leverages light field imaging to retrieve forward-scattered photons as signals to overcome the challenges of low-SNR imaging caused by super-strong scattering environments. This model aims to recover the ballistic photon signal as a source term from forward-scattered photons based on diffusion equations. The DLIM consists of two main processes: radiance modeling and diffusion light-field approximation. Radiate modeling analyzes the radiance distribution in scattering light field images using a proposed three-plane parameterization, which solves a 4-D radiate kernel describing the impulse function of scattering light field. Then, the scattering light field images synthesize a diffuse source satisfying the diffusion equation governing forward scattering photons, solved under Neumann boundary conditions in imaging space. This is the first physically-aware scattering light field imaging model, extending the conventional light field imaging framework from free space into diffuse space. The extensive experiments confirm that the DLIM can reconstruct the target objects even when scattering light field images are reduced as random noise at extremely low SNRs.
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