no code implementations • 16 Mar 2024 • Che-Yung Shen, Jingxi Li, Tianyi Gan, Yuhang Li, Langxing Bai, Mona Jarrahi, Aydogan Ozcan
These wavelength-multiplexed patterns are projected onto a single field-of-view (FOV) at the output plane of the diffractive processor, enabling the capture of quantitative phase distributions of input objects located at different axial planes using an intensity-only image sensor.
no code implementations • 4 Feb 2024 • Guangdong Ma, Xilin Yang, Bijie Bai, Jingxi Li, Yuhang Li, Tianyi Gan, Che-Yung Shen, Yijie Zhang, Yuzhu Li, Mona Jarrahi, Aydogan Ozcan
We demonstrated the feasibility of this reconfigurable multiplexed diffractive design by approximating 256 randomly selected permutation matrices using K=4 rotatable diffractive layers.
no code implementations • 30 Jan 2024 • Jingxi Li, Yuhang Li, Tianyi Gan, Che-Yung Shen, Mona Jarrahi, Aydogan Ozcan
Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing.
no code implementations • 17 Jan 2024 • Jingtian Hu, Kun Liao, Niyazi Ulas Dinc, Carlo Gigli, Bijie Bai, Tianyi Gan, Xurong Li, Hanlong Chen, Xilin Yang, Yuhang Li, Cagatay Isil, Md Sadman Sakib Rahman, Jingxi Li, Xiaoyong Hu, Mona Jarrahi, Demetri Psaltis, Aydogan Ozcan
To resolve subwavelength features of an object, the diffractive imager uses a thin, high-index solid-immersion layer to transmit high-frequency information of the object to a spatially-optimized diffractive encoder, which converts/encodes high-frequency information of the input into low-frequency spatial modes for transmission through air.
no code implementations • 8 Nov 2023 • Che-Yung Shen, Jingxi Li, Tianyi Gan, Mona Jarrahi, Aydogan Ozcan
Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with various applications ranging from imaging to beam focusing.
no code implementations • 5 Oct 2023 • Xilin Yang, Md Sadman Sakib Rahman, Bijie Bai, Jingxi Li, Aydogan Ozcan
Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are non-negative, acting on diffraction-limited optical intensity patterns at the input field-of-view (FOV).
no code implementations • 17 Sep 2023 • Cagatay Isil, Tianyi Gan, F. Onuralp Ardic, Koray Mentesoglu, Jagrit Digani, Huseyin Karaca, Hanlong Chen, Jingxi Li, Deniz Mengu, Mona Jarrahi, Kaan Akşit, Aydogan Ozcan
Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%.
no code implementations • 29 Aug 2023 • Bijie Bai, Xilin Yang, Tianyi Gan, Jingxi Li, Deniz Mengu, Mona Jarrahi, Aydogan Ozcan
Our analyses revealed the efficacy of this P-D2NN design in unidirectional image magnification and demagnification tasks, producing high-fidelity magnified or demagnified images in only one direction, while inhibiting the image formation in the opposite direction - confirming the desired unidirectional imaging operation.
no code implementations • 5 Aug 2023 • Che-Yung Shen, Jingxi Li, Deniz Mengu, Aydogan Ozcan
Here, we present the design of a diffractive processor that can all-optically perform multispectral quantitative phase imaging of transparent phase-only objects in a snapshot.
no code implementations • 2 Aug 2023 • Yuzhu Li, Nir Pillar, Jingxi Li, Tairan Liu, Di wu, Songyu Sun, Guangdong Ma, Kevin De Haan, Luzhe Huang, Sepehr Hamidi, Anatoly Urisman, Tal Keidar Haran, William Dean Wallace, Jonathan E. Zuckerman, Aydogan Ozcan
Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time.
no code implementations • 12 Apr 2023 • Yuhang Li, Jingxi Li, Yifan Zhao, Tianyi Gan, Jingtian Hu, Mona Jarrahi, Aydogan Ozcan
We demonstrate universal polarization transformers based on an engineered diffractive volume, which can synthesize a large set of arbitrarily-selected, complex-valued polarization scattering matrices between the polarization states at different positions within its input and output field-of-views (FOVs).
no code implementations • 23 Mar 2023 • Md Sadman Sakib Rahman, Xilin Yang, Jingxi Li, Bijie Bai, Aydogan Ozcan
Under spatially-coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number (N) of optimizable phase-only diffractive features is greater than or equal to ~2 Ni x No, where Ni and No refer to the number of useful pixels at the input and the output FOVs, respectively.
no code implementations • 5 Dec 2022 • Jingxi Li, Tianyi Gan, Yifan Zhao, Bijie Bai, Che-Yung Shen, Songyu Sun, Mona Jarrahi, Aydogan Ozcan
A unidirectional imager would only permit image formation along one direction, from an input field-of-view (FOV) A to an output FOV B, and in the reverse path, the image formation would be blocked.
no code implementations • 13 Aug 2022 • Jingxi Li, Bijie Bai, Yi Luo, Aydogan Ozcan
We report deep learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily-selected, complex-valued linear transformations between an input and output field-of-view, each with N_i and N_o pixels, respectively.
no code implementations • 30 Jun 2022 • Tairan Liu, Yuzhu Li, Hatice Ceylan Koydemir, Yijie Zhang, Ethan Yang, Merve Eryilmaz, Hongda Wang, Jingxi Li, Bijie Bai, Guangdong Ma, Aydogan Ozcan
We also demonstrated that this data-driven plaque assay offers the capability of quantifying the infected area of the cell monolayer, performing automated counting and quantification of PFUs and virus-infected areas over a 10-fold larger dynamic range of virus concentration than standard viral plaque assays.
no code implementations • 15 Jun 2022 • Cagatay Isil, Deniz Mengu, Yifan Zhao, Anika Tabassum, Jingxi Li, Yi Luo, Mona Jarrahi, Aydogan Ozcan
We report a deep learning-enabled diffractive display design that is based on a jointly-trained pair of an electronic encoder and a diffractive optical decoder to synthesize/project super-resolved images using low-resolution wavefront modulators.
no code implementations • 25 Mar 2022 • Jingxi Li, Yi-Chun Hung, Onur Kulce, Deniz Mengu, Aydogan Ozcan
The transmission layers of this polarization multiplexed diffractive network are trained and optimized via deep learning and error-backpropagation by using thousands of examples of the input/output fields corresponding to each one of the complex-valued linear transformations assigned to different input/output polarization combinations.
no code implementations • 8 Dec 2021 • Bijie Bai, Hongda Wang, Yuzhu Li, Kevin De Haan, Francesco Colonnese, Yujie Wan, Jingyi Zuo, Ngan B. Doan, Xiaoran Zhang, Yijie Zhang, Jingxi Li, Wenjie Dong, Morgan Angus Darrow, Elham Kamangar, Han Sung Lee, Yair Rivenson, Aydogan Ozcan
The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis.
no code implementations • 15 Sep 2020 • Md Sadman Sakib Rahman, Jingxi Li, Deniz Mengu, Yair Rivenson, Aydogan Ozcan
A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.
no code implementations • 30 Jun 2020 • Muhammed Veli, Deniz Mengu, Nezih T. Yardimci, Yi Luo, Jingxi Li, Yair Rivenson, Mona Jarrahi, Aydogan Ozcan
Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics.
no code implementations • 15 May 2020 • Jingxi Li, Deniz Mengu, Nezih T. Yardimci, Yi Luo, Xurong Li, Muhammed Veli, Yair Rivenson, Mona Jarrahi, Aydogan Ozcan
3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction.
no code implementations • 24 Jan 2020 • Wei Wang, Shibo Zhou, Jingxi Li, Xiaohua LI, Junsong Yuan, Zhanpeng Jin
Accurate real-time object recognition from sensory data has long been a crucial and challenging task for autonomous driving.
no code implementations • 20 Jan 2020 • Yijie Zhang, Kevin De Haan, Yair Rivenson, Jingxi Li, Apostolos Delis, Aydogan Ozcan
This approach uses a single deep neural network that receives two different sources of information at its input: (1) autofluorescence images of the label-free tissue sample, and (2) a digital staining matrix which represents the desired microscopic map of different stains to be virtually generated at the same tissue section.
no code implementations • 8 Jun 2019 • Jingxi Li, Deniz Mengu, Yi Luo, Yair Rivenson, Aydogan Ozcan
Similar to ensemble methods practiced in machine learning, we also independently-optimized multiple differential diffractive networks that optically project their light onto a common detector plane, and achieved testing accuracies of 98. 59%, 91. 06% and 51. 44% for MNIST, Fashion-MNIST and grayscale CIFAR-10, respectively.