291 citations · 2.5k across the 25 of their papers we have counts for
25 papers
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…
Pyramid diffractive optical networks for unidirectional image magnification and demagnification
Bijie Bai, Xilin Yang, Tianyi Gan +4
Diffractive deep neural networks (D2NNs) are composed of successive transmissive layers optimized using supervised deep learning to all-optically implement various computational ta…
Plasmonic photoconductive terahertz focal-plane array with pixel super-resolution
Xurong Li, Deniz Mengu, Aydogan Ozcan +1
Imaging systems operating in the terahertz part of the electromagnetic spectrum are in great demand because of the distinct characteristics of terahertz waves in penetrating many o…
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…
Data class-specific all-optical transformations and encryption
Bijie Bai, Heming Wei, Xilin Yang +2
Diffractive optical networks provide rich opportunities for visual computing tasks since the spatial information of a scene can be directly accessed by a diffractive processor with…
Snapshot Multispectral Imaging Using a Diffractive Optical Network
Deniz Mengu, Anika Tabassum, Mona Jarrahi +1
Multispectral imaging has been used for numerous applications in e.g., environmental monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical network…
Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks
Deniz Mengu, Yifan Zhao, Anika Tabassum +2
Permutation matrices form an important computational building block frequently used in various fields including e.g., communications, information security and data processing. Opti…
Super-resolution image display using diffractive decoders
Cagatay Isil, Deniz Mengu, Yifan Zhao +5
High-resolution synthesis/projection of images over a large field-of-view (FOV) is hindered by the restricted space-bandwidth-product (SBP) of wavefront modulators. We report a dee…
To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects
Bijie Bai, Yi Luo, Tianyi Gan +6
Privacy protection is a growing concern in the digital era, with machine vision techniques widely used throughout public and private settings. Existing methods address this growing…
Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network
Jingxi Li, Yi-Chun Hung, Onur Kulce +2
Research on optical computing has recently attracted significant attention due to the transformative advances in machine learning. Among different approaches, diffractive optical n…
Diffractive all-optical computing for quantitative phase imaging
Deniz Mengu, Aydogan Ozcan
Quantitative phase imaging (QPI) is a label-free computational imaging technique that provides optical path length information of specimens. In modern implementations, the quantita…
Cascadable all-optical NAND gates using diffractive networks
Yi Luo, Deniz Mengu, Aydogan Ozcan
Owing to its potential advantages such as scalability, low latency and power efficiency, optical computing has seen rapid advances over the last decades. A core unit of a potential…
All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfaces
Onur Kulce, Deniz Mengu, Yair Rivenson +1
We report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input (N_i) and output (N_o), where N_i and N_o rep…
Classification and reconstruction of spatially overlapping phase images using diffractive optical networks
Deniz Mengu, Muhammed Veli, Yair Rivenson +1
Diffractive optical networks unify wave optics and deep learning to all-optically compute a given machine learning or computational imaging task as the light propagates from the in…
Scale-, shift- and rotation-invariant diffractive optical networks
Deniz Mengu, Yair Rivenson, Aydogan Ozcan
Recent research efforts in optical computing have gravitated towards developing optical neural networks that aim to benefit from the processing speed and parallelism of optics/phot…
Ensemble learning of diffractive optical networks
Md Sadman Sakib Rahman, Jingxi Li, Deniz Mengu +2
A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning. Specifically, there has been a reviva…
All-Optical Information Processing Capacity of Diffractive Surfaces
Onur Kulce, Deniz Mengu, Yair Rivenson +1
Precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and photonics. These advances around the engineering of materials with…
Terahertz Pulse Shaping Using Diffractive Surfaces
Muhammed Veli, Deniz Mengu, Nezih T. Yardimci +5
Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics. At the intersection of machine learning and optics, diffractive…
Misalignment Resilient Diffractive Optical Networks
Deniz Mengu, Yifan Zhao, Nezih T. Yardimci +3
As an optical machine learning framework, Diffractive Deep Neural Networks (D2NN) take advantage of data-driven training methods used in deep learning to devise light-matter intera…
Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
Jingxi Li, Deniz Mengu, Nezih T. Yardimci +6
3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction. Here, we demonstrate the des…
Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks
Yi Luo, Deniz Mengu, Nezih T. Yardimci +4
We report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally-incoherent broadband source to al…
Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy
Jingxi Li, Deniz Mengu, Yi Luo +2
Diffractive deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all…
Response to Comment on "All-optical machine learning using diffractive deep neural networks"
Deniz Mengu, Yi Luo, Yair Rivenson +3
In their Comment, Wei et al. (arXiv:1809.08360v1 [cs.LG]) claim that our original interpretation of Diffractive Deep Neural Networks (D2NN) represent a mischaracterization of the s…