111 citations · 460 across the 12 of their papers we have counts for
12 papers
Super-resolution image projection over an extended depth of field using a diffractive decoder
Hanlong Chen, Cagatay Isil, Tianyi Gan +2
Image projection systems must be efficient in data storage, computation and transmission while maintaining a large space-bandwidth-product (SBP) at their output. Here, we introduce…
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…
Optical Generative Models
Shiqi Chen, Yuhang Li, Hanlong Chen +1
Generative models cover various application areas, including image, video and music synthesis, natural language processing, and molecular design, among many others. As digital gene…
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…
BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images
Michael John Fanous, Christopher Michael Seybold, Hanlong Chen +2
We developed a rapid scanning optical microscope, termed "BlurryScope", that leverages continuous image acquisition and deep learning to provide a cost-effective and compact soluti…
Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processor
Jingtian Hu, Kun Liao, Niyazi Ulas Dinc +14
Phase imaging is widely used in biomedical imaging, sensing, and material characterization, among other fields. However, direct imaging of phase objects with subwavelength resoluti…
All-optical image denoising using a diffractive visual processor
Cagatay Isıl, Tianyi Gan, F. Onuralp Ardic +9
Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers,…
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…
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…
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-…