用于计算成像中多功能且可泛化大规模重建的局部条件神经场
Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging
AI总结：
针对传统像素表征难以捕捉物体连续多尺度细节的问题，提出局部条件神经场LCNF框架，通过局部条件表征与编解码器结构，实现FPM大规模鲁棒可泛化的相位重建，可应用于生物样本实验测量。
摘要：
深度学习已革新了计算成像领域，但传统基于像素的表征方式限制了其捕捉物体连续、多尺度细节的能力。本文提出了一种新颖的局部条件神经场（Local Conditional Neural Fields, LCNF）框架，利用连续隐式神经表征来解决这一局限。LCNF可实现灵活的物体表征，并促进多尺度信息的重建。\n我们验证了LCNF在解决傅里叶叠层显微术（Fourier ptychographic microscopy, FPM）中带多路复用测量的高病态逆问题方面的能力，实现了鲁棒、可扩展且可泛化的大规模相位恢复。与传统神经场框架不同，LCNF引入了局部条件表征，可提升模型泛化能力、学习多尺度信息，并高效处理大规模成像数据。通过结合以学习到的隐向量为条件的编码器和解码器，LCNF可实现多功能的连续域超分辨率图像重建。\n我们证明，仅需少量多路复用测量，LCNF就能准确重建大视场、高分辨率的相位图像。即使在不完美数据集上训练，LCNF也能鲁棒地捕捉连续物体先验并消除各类相位伪影。该框架展现出强泛化性，即便训练数据有限，也能重建多样化的物体。此外，LCNF可在使用自然图像的物理模拟器上训练，并成功应用于生物样本的实验测量。我们的研究结果凸显了LCNF在解决计算成像中大规模逆问题方面的潜力，其可广泛应用于各类基于深度学习的技术中。
英文摘要：
Deep learning has transformed computational imaging, but traditional pixel-based representations limit their ability to capture continuous, multiscale details of objects. Here we introduce a novel Local Conditional Neural Fields (LCNF) framework, leveraging a continuous implicit neural representation to address this limitation. LCNF enables flexible object representation and facilitates the reconstruction of multiscale information. We demonstrate the capabilities of LCNF in solving the highly ill-posed inverse problem in Fourier ptychographic microscopy (FPM) with multiplexed measurements, achieving robust, scalable, and generalizable large-scale phase retrieval. Unlike traditional neural fields frameworks, LCNF incorporates a local conditional representation that promotes model generalization, learning multiscale information, and efficient processing of large-scale imaging data. By combining an encoder and a decoder conditioned on a learned latent vector, LCNF achieves versatile continuous-domain super-resolution image reconstruction. We demonstrate accurate reconstruction of wide field-of-view, high-resolution phase images using only a few multiplexed measurements. LCNF robustly captures the continuous object priors and eliminates various phase artifacts, even when it is trained on imperfect datasets. The framework exhibits strong generalization, reconstructing diverse objects even with limited training data. Furthermore, LCNF can be trained on a physics simulator using natural images and successfully applied to experimental measurements on biological samples. Our results highlight the potential of LCNF for solving large-scale inverse problems in computational imaging, with broad applicability in various deep-learning-based techniques.
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