用隐式神经表示直接学相位函数，让光场聚焦更精准
Implicitly Learned Neural Phase Functions for Basis-Free Point Spread Function Engineering
- 用隐式神经网络直接建模相位函数，摆脱传统基函数限制
- 对随机相位函数重建，中位MSSIM达0.8162，均值提升至0.5634
- 适合需要高精度点扩散函数设计的显微成像与生物光子学研究
点扩散函数（PSF）工程对计算成像中光聚焦控制至关重要，广泛应用于神经成像、荧光显微镜和生物光子学。由于PSF是相位函数傅里叶变换模长的结果，从给定PSF重构相位函数属于病态逆问题。传统方法依赖物理基函数，难以泛化到多样化的成像需求。本文提出一种基于隐式神经表示的新方法，克服了像素级优化的局限性。在学习随机生成的相位函数时，该方法中位MSSIM达0.8162，均值为0.5634，而像素级优化仅为中位0.0、均值0.1841；同时中位PSNR达10.38 dB，均值8.672 dB，显著优于像素级优化的中位6.653 dB与均值6.660 dB。
原文摘要 · Abstract (English)
Point spread function (PSF) engineering is vital for precisely controlling the focus of light in computational imaging, with applications in neural imaging, fluorescence microscopy, and biophotonics. The PSF is derived from the magnitude of the Fourier transform of a phase function, making the construction of the phase function given the PSF (PSF engineering) an ill-posed inverse problem. Traditional PSF engineering methods rely on physical basis functions, limiting their ability to generalize across the range of PSFs required for imaging tasks. We introduce a novel approach leveraging implicit neural representations that overcome the limitations of pixel-wise optimization methods. Our approach achieves a median MSSIM of 0.8162 and a mean MSSIM of 0.5634, compared to a median MSSIM of 0.0 and a mean MSSIM of 0.1841 with pixel-wise optimization when learning randomly generated phase functions. Our approach also achieves a median PSNR of 10.38 dB and a mean PSNR of 8.672 dB, compared to a median PSNR of 6.653 dB and a mean PSNR of 6.660 dB with pixel-wise optimization for this task.
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