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.

翻译：深度学习已革新了计算成像领域，但传统基于像素的表示方法限制了其对物体连续多尺度细节的捕捉能力。本文提出一种新颖的局部条件神经场（LCNF）框架，通过利用连续隐式神经表示来解决这一局限性。LCNF支持灵活的目标表示，并促进多尺度信息的重建。我们展示了LCNF在解决傅里叶叠层显微术（FPM）中高度病态逆问题（含复用测量）时的能力，实现了鲁棒、可扩展且通用的规模化相位恢复。与传统神经场框架不同，LCNF采用局部条件表示，增强了模型泛化能力、多尺度信息学习能力以及对大规模成像数据的高效处理能力。通过结合基于学习潜向量的编码器与条件解码器，LCNF实现了灵活的连续域超分辨率图像重建。我们仅利用少量复用测量即实现宽视场、高分辨率相位图像的精确重建。即使在不完美数据集上训练，LCNF也能稳健捕捉连续目标先验并消除各类相位伪影。该框架展现出强大泛化性，在有限训练数据下仍能重建多样化目标。此外，LCNF可在基于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。我们的结果凸显了LCNF在解决计算成像领域大规模逆问题中的潜力，对各类深度学习技术具有广泛适用性。