Kavli Affiliate: Changhuei Yang
| First 5 Authors: Haowen Zhou, Brandon Y. Feng, Haiyun Guo, Siyu Lin, Mingshu Liang
| Summary:
Image stacks provide invaluable 3D information in various biological and
pathological imaging applications. Fourier ptychographic microscopy (FPM)
enables reconstructing high-resolution, wide field-of-view image stacks without
z-stack scanning, thus significantly accelerating image acquisition. However,
existing FPM methods take tens of minutes to reconstruct and gigabytes of
memory to store a high-resolution volumetric scene, impeding fast
gigapixel-scale remote digital pathology. While deep learning approaches have
been explored to address this challenge, existing methods poorly generalize to
novel datasets and can produce unreliable hallucinations. This work presents
FPM-INR, a compact and efficient framework that integrates physics-based
optical models with implicit neural representations (INR) to represent and
reconstruct FPM image stacks. FPM-INR is agnostic to system design or sample
types and does not require external training data. In our demonstrated
experiments, FPM-INR substantially outperforms traditional FPM algorithms with
up to a 25-fold increase in speed and an 80-fold reduction in memory usage for
continuous image stack representations.
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