354 citations · 2.8k across the 40 of their papers we have counts for
13 papers · 1 filter
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,…
Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
Che-Yung Shen, Jingxi Li, Deniz Mengu +1
As a label-free imaging technique, quantitative phase imaging (QPI) provides optical path length information of transparent specimens for various applications in biology, materials…
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…
Broadband nonlinear modulation of incoherent light using a transparent optoelectronic neuron array
Dehui Zhang, Dong Xu, Yuhang Li +14
Nonlinear optical processing of ambient natural light is highly desired in computational imaging and sensing applications. A strong optical nonlinear response that can work under w…
Learning Diffractive Optical Communication Around Arbitrary Opaque Occlusions
Md Sadman Sakib Rahman, Tianyi Gan, Emir Arda Deger +3
Free-space optical systems are emerging for high data rate communication and transfer of information in indoor and outdoor settings. However, free-space optical communication becom…
Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer
Yuhang Li, Jingxi Li, Yifan Zhao +4
We demonstrate universal polarization transformers based on an engineered diffractive volume, which can synthesize a large set of arbitrarily-selected, complex-valued polarization…
Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding
Yuhang Li, Tianyi Gan, Bijie Bai +3
Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due…
Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors
Md Sadman Sakib Rahman, Xilin Yang, Jingxi Li +2
Under spatially-coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between…
Rapid Sensing of Hidden Objects and Defects using a Single-Pixel Diffractive Terahertz Processor
Jingxi Li, Xurong Li, Nezih T. Yardimci +6
Terahertz waves offer numerous advantages for the nondestructive detection of hidden objects/defects in materials, as they can penetrate through most optically-opaque materials. Ho…
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian +72
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasin…
Deep learning-enabled multiplexed point-of-care sensor using a paper-based fluorescence vertical flow assay
Artem Goncharov, Hyou-Arm Joung, Rajesh Ghosh +8
We demonstrate multiplexed computational sensing with a point-of-care serodiagnosis assay to simultaneously quantify three biomarkers of acute cardiac injury. This point-of-care se…
Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network
Yuhang Li, Yi Luo, Deniz Mengu +2
Quantitative phase imaging (QPI) is a label-free computational imaging technique used in various fields, including biology and medical research. Modern QPI systems typically rely o…
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…