111 citations · 731 across the 19 of their papers we have counts for
20 papers
Snapshot multi-spectral imaging through defocusing and a Fourier imager network
Xilin Yang, Michael John Fanous, Hanlong Chen +6
Multi-spectral imaging, which simultaneously captures the spatial and spectral information of a scene, is widely used across diverse fields, including remote sensing, biomedical im…
Virtual Staining of Label-Free Tissue in Imaging Mass Spectrometry
Yijie Zhang, Luzhe Huang, Nir Pillar +5
Imaging mass spectrometry (IMS) is a powerful tool for untargeted, highly multiplexed molecular mapping of tissue in biomedical research. IMS offers a means of mapping the spatial…
Multi-scale Generative Modeling for Fast Sampling
Xiongye Xiao, Shixuan Li, Luzhe Huang +6
While working within the spatial domain can pose problems associated with ill-conditioned scores caused by power-law decay, recent advances in diffusion-based generative models hav…
Pixel super-resolved virtual staining of label-free tissue using diffusion models
Yijie Zhang, Luzhe Huang, Nir Pillar +3
Virtual staining of tissue offers a powerful tool for transforming label-free microscopy images of unstained tissue into equivalents of histochemically stained samples. This study…
Unidirectional imaging with partially coherent light
Guangdong Ma, Che-Yung Shen, Jingxi Li +8
Unidirectional imagers form images of input objects only in one direction, e.g., from field-of-view (FOV) A to FOV B, while blocking the image formation in the reverse direction, f…
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration
Luzhe Huang, Xiongye Xiao, Shixuan Li +4
The advance of diffusion-based generative models in recent years has revolutionized state-of-the-art (SOTA) techniques in a wide variety of image analysis and synthesis tasks, wher…
A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology
Luzhe Huang, Yuzhu Li, Nir Pillar +3
Histopathological staining of human tissue is essential for disease diagnosis. Recent advances in virtual tissue staining technologies using artificial intelligence (AI) alleviate…
Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Data
Michael John Fanous, Paloma Casteleiro Costa, Cagatay Isil +2
The integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging. A compelling trend in this field involves deliberately compromising certa…
Virtual histological staining of unlabeled autopsy tissue
Yuzhu Li, Nir Pillar, Jingxi Li +12
Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior s…
Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems
Luzhe Huang, Jianing Li, Xiaofu Ding +3
Uncertainty estimation is critical for numerous applications of deep neural networks and draws growing attention from researchers. Here, we demonstrate an uncertainty quantificatio…
eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging
Hanlong Chen, Luzhe Huang, Tairan Liu +1
The application of deep learning techniques has greatly enhanced holographic imaging capabilities, leading to improved phase recovery and image reconstruction. Here, we introduce a…
Self-supervised learning of hologram reconstruction using physics consistency
Luzhe Huang, Hanlong Chen, Tairan Liu +1
The past decade has witnessed transformative applications of deep learning in various computational imaging, sensing and microscopy tasks. Due to the supervised learning schemes em…
Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks
Yijie Zhang, Luzhe Huang, Tairan Liu +5
Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming,…
Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
Hanlong Chen, Luzhe Huang, Tairan Liu +1
Deep learning-based image reconstruction methods have achieved remarkable success in phase recovery and holographic imaging. However, the generalization of their image reconstructi…
Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network
Luzhe Huang, Xilin Yang, Tairan Liu +1
Deep learning-based methods in computational microscopy have been shown to be powerful but in general face some challenges due to limited generalization to new types of samples and…
Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks
Luzhe Huang, Tairan Liu, Xilin Yang +3
Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging. Recovery of the missing phase information of a hologram is an important st…
Deep learning-based virtual refocusing of images using an engineered point-spread function
Xilin Yang, Luzhe Huang, Yilin Luo +4
We present a virtual image refocusing method over an extended depth of field (DOF) enabled by cascaded neural networks and a double-helix point-spread function (DH-PSF). This netwo…
Recurrent neural network-based volumetric fluorescence microscopy
Luzhe Huang, Yilin Luo, Yair Rivenson +1
Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-…
Single-shot autofocusing of microscopy images using deep learning
Yilin Luo, Luzhe Huang, Yair Rivenson +1
We demonstrate a deep learning-based offline autofocusing method, termed Deep-R, that is trained to rapidly and blindly autofocus a single-shot microscopy image of a specimen that…
SNR-adaptive OCT angiography enabled by statistical characterization of intensity and decorrelation with multi-variate time series model
Luzhe Huang, Yiming Fu, Ruixiang Chen +6
In OCT angiography (OCTA), decorrelation computation has been widely used as a local motion index to identify dynamic flow from static tissues, but its dependence on SNR severely d…