Can I read Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging on EtoBox?

Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging by Wang, Hao; Zhu, Jiabei; Li, Yunzhe; Yang, QianWan; Tian, Lei is a scholarly article available to read on EtoBox.

What is Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging about?

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

Author

Wang, Hao; Zhu, Jiabei; Li, Yunzhe; Yang, QianWan; Tian, Lei

Published

2023

Language

EN