Resolution-Enhanced Parallel Coded Ptychography for High-Throughput Optical Imaging

摄影术 光学 吞吐量 计算机科学 分辨率(逻辑) 衍射 材料科学 物理 人工智能 电信 无线
作者
Shaowei Jiang,Chengfei Guo,Pengming Song,Niyun Zhou,Zichao Bian,Jiakai Zhu,Ruihai Wang,Pei Dong,Zibang Zhang,Jun Liao,Jianhua Yao,Bin Feng,Michael J. Murphy,Guoan Zheng
出处
期刊:ACS Photonics [American Chemical Society]
卷期号:8 (11): 3261-3271 被引量:55
标识
DOI:10.1021/acsphotonics.1c01085
摘要

Ptychography is an enabling coherent diffraction imaging technique for both fundamental and applied sciences. Its applications in optical microscopy, however, fall short for its low imaging throughput and limited resolution. Here, we report a resolution-enhanced parallel coded ptychography technique achieving the highest numerical aperture and an imaging throughput orders of magnitude greater than previous demonstrations. In this platform, we translate the samples across the disorder-engineered surfaces for lensless diffraction data acquisition. The engineered surface consists of chemically etched micron-level phase scatters and printed sub-wavelength intensity absorbers. It is designed to unlock an optical space with spatial extent (x, y) and frequency content (kx, ky) that is inaccessible using conventional lens-based optics. To achieve the best resolution performance, we also report a new coherent diffraction imaging model by considering both the spatial and angular responses of the pixel readouts. Our low-cost prototype can directly resolve 308-nm linewidth on the resolution target without aperture synthesizing. Gigapixel high-resolution microscopic images with a 240-mm^2 effective field of view can be acquired in 15 seconds. For demonstrations, we recover slow-varying 3D phase objects with many 2{\pi} wraps, including optical prism and convex lens. The low-frequency phase contents of these objects are challenging to obtain using other existing lensless techniques. For digital pathology applications, we perform accurate virtual staining by using the recovered phase as attention guidance in a deep neural network. Parallel optical processing using the reported technique enables novel optical instruments with inherent quantitative nature and metrological versatility.
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