稳健性(进化)
迭代重建
光学
图像分辨率
计算机科学
显微镜
物理
人工神经网络
信噪比(成像)
噪音(视频)
人工智能
重建算法
算法
计算机视觉
图像(数学)
化学
生物化学
基因
作者
Siying Wang,Chen Bai,Xing Li,Jia Qian,Runze Li,Tong Peng,Xuan Tian,Wang Ma,Rui Ma,Sha An,Peng Gao,Dan Dan,Baoli Yao
出处
期刊:Optics Letters
[The Optical Society]
日期:2024-08-06
卷期号:49 (17): 4855-4855
摘要
With full-field imaging and high photon efficiency advantages, structured illumination microscopy (SIM) is one of the most potent super-resolution (SR) modalities in bioscience. Regarding SR reconstruction for SIM, spatial domain reconstruction (SDR) has been proven to be faster than traditional frequency domain reconstruction (FDR), facilitating real-time imaging of live cells. Nevertheless, SDR relies on high-precision parameter estimation for reconstruction, which tends to suffer from low signal-to-noise ratio (SNR) conditions and inevitably leads to artifacts that seriously affect the accuracy of SR reconstruction. In this Letter, a physics-enhanced neural network-based parameter-free SDR (PNNP-SDR) is proposed, which can achieve SR reconstruction directly in the spatial domain. As a result, the peak-SNR (PSNR) of PNNP-SDR is improved by about 4 dB compared to the cross-correlation (COR) SR reconstruction; meanwhile, the reconstruction speed of PNNP-SDR is even about five times faster than the fast approach based on principal component analysis (PCA). Given its capability of achieving parameter-free imaging, noise robustness, and high-fidelity and high-speed SR reconstruction over conventional SIM microscope hardware, the proposed PNNP-SDR is expected to be widely adopted in biomedical SR imaging scenarios.
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