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Zhang Y, Zhang Q, Yu H, Zhang Y, Luan H, Gu M. Memory-less scattering imaging with ultrafast convolutional optical neural networks. SCIENCE ADVANCES 2024; 10:eadn2205. [PMID: 38875337 PMCID: PMC11177939 DOI: 10.1126/sciadv.adn2205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 05/13/2024] [Indexed: 06/16/2024]
Abstract
The optical memory effect in complex scattering media including turbid tissue and speckle layers has been a critical foundation for macroscopic and microscopic imaging methods. However, image reconstruction from strong scattering media without the optical memory effect has not been achieved. Here, we demonstrate image reconstruction through scattering layers where no optical memory effect exists, by developing a multistage convolutional optical neural network (ONN) integrated with multiple parallel kernels operating at the speed of light. Training this Fourier optics-based, parallel, one-step convolutional ONN with the strong scattering process for direct feature extraction, we achieve memory-less image reconstruction with a field of view enlarged by a factor up to 271. This device is dynamically reconfigurable for ultrafast multitask image reconstruction with a computational power of 1.57 peta-operations per second (POPS). Our achievement establishes an ultrafast and high energy-efficient optical machine learning platform for graphic processing.
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Affiliation(s)
- Yuchao Zhang
- Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Qiming Zhang
- Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Haoyi Yu
- Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yinan Zhang
- Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Haitao Luan
- Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Min Gu
- Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China
- Zhangjiang Laboratory, Shanghai 200093, China
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Li J, Li Y, Gan T, Shen CY, Jarrahi M, Ozcan A. All-optical complex field imaging using diffractive processors. LIGHT, SCIENCE & APPLICATIONS 2024; 13:120. [PMID: 38802376 PMCID: PMC11130282 DOI: 10.1038/s41377-024-01482-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/11/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024]
Abstract
Complex field imaging, which captures both the amplitude and phase information of input optical fields or objects, can offer rich structural insights into samples, such as their absorption and refractive index distributions. However, conventional image sensors are intensity-based and inherently lack the capability to directly measure the phase distribution of a field. This limitation can be overcome using interferometric or holographic methods, often supplemented by iterative phase retrieval algorithms, leading to a considerable increase in hardware complexity and computational demand. 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. Our design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field, forming two independent imaging channels that perform amplitude-to-amplitude and phase-to-intensity transformations between the input and output planes within a compact optical design, axially spanning ~100 wavelengths. The intensity distributions of the output fields at these two channels on the sensor plane directly correspond to the amplitude and quantitative phase profiles of the input complex field, eliminating the need for any digital image reconstruction algorithms. We experimentally validated the efficacy of our complex field diffractive imager designs through 3D-printed prototypes operating at the terahertz spectrum, with the output amplitude and phase channel images closely aligning with our numerical simulations. We envision that this complex field imager will have various applications in security, biomedical imaging, sensing and material science, among others.
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Affiliation(s)
- Jingxi Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Yuhang Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Tianyi Gan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Che-Yung Shen
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Mona Jarrahi
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA.
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Hu J, Mengu D, Tzarouchis DC, Edwards B, Engheta N, Ozcan A. Diffractive optical computing in free space. Nat Commun 2024; 15:1525. [PMID: 38378715 PMCID: PMC10879514 DOI: 10.1038/s41467-024-45982-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 02/09/2024] [Indexed: 02/22/2024] Open
Abstract
Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light on the potential of free-space optical systems based on engineered surfaces for advancing optical computing. Manipulating light in unprecedented ways, emerging structured surfaces enable all-optical implementation of various mathematical functions and machine learning tasks. Diffractive networks, in particular, bring deep-learning principles into the design and operation of free-space optical systems to create new functionalities. Metasurfaces consisting of deeply subwavelength units are achieving exotic optical responses that provide independent control over different properties of light and can bring major advances in computational throughput and data-transfer bandwidth of free-space optical processors. Unlike integrated photonics-based optoelectronic systems that demand preprocessed inputs, free-space optical processors have direct access to all the optical degrees of freedom that carry information about an input scene/object without needing digital recovery or preprocessing of information. To realize the full potential of free-space optical computing architectures, diffractive surfaces and metasurfaces need to advance symbiotically and co-evolve in their designs, 3D fabrication/integration, cascadability, and computing accuracy to serve the needs of next-generation machine vision, computational imaging, mathematical computing, and telecommunication technologies.
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Affiliation(s)
- Jingtian Hu
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Deniz Mengu
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Dimitrios C Tzarouchis
- Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Meta Materials Inc., Athens, 15123, Greece
| | - Brian Edwards
- Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nader Engheta
- Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA.
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Ouyang H, Zhao Z, Tao Z, You J, Cheng X, Jiang T. Parallel edge extraction operators on chip speed up photonic convolutional neural networks. OPTICS LETTERS 2024; 49:838-841. [PMID: 38359195 DOI: 10.1364/ol.517583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 01/17/2024] [Indexed: 02/17/2024]
Abstract
We experimentally establish a 3 × 3 cross-shaped micro-ring resonator (MRR) array-based photonic multiplexing architecture relying on silicon photonics to achieve parallel edge extraction operations in images for photonic convolution neural networks. The main mathematical operations involved are convolution. Precisely, a faster convolutional calculation speed of up to four times is achieved by extracting four feature maps simultaneously with the same photonic hardware's structure and power consumption, where a maximum computility of 0.742 TOPS at an energy cost of 48.6 mW and a convolution accuracy of 95.1% is achieved in an MRR array chip. In particular, our experimental results reveal that this system using parallel edge extraction operators instead of universal operators can improve the imaging recognition accuracy for CIFAR-10 dataset by 6.2% within the same computing time, reaching a maximum of 78.7%. This work presents high scalability and efficiency of parallel edge extraction chips, furnishing a novel, to the best of our knowledge, approach to boost photonic computing speed.
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Işıl Ç, Gan T, Ardic FO, Mentesoglu K, Digani J, Karaca H, Chen H, Li J, Mengu D, Jarrahi M, Akşit K, Ozcan A. All-optical image denoising using a diffractive visual processor. LIGHT, SCIENCE & APPLICATIONS 2024; 13:43. [PMID: 38310118 PMCID: PMC10838318 DOI: 10.1038/s41377-024-01385-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 01/14/2024] [Accepted: 01/15/2024] [Indexed: 02/05/2024]
Abstract
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, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. 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%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.
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Affiliation(s)
- Çağatay Işıl
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Tianyi Gan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Fazil Onuralp Ardic
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
| | - Koray Mentesoglu
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
| | - Jagrit Digani
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
| | - Huseyin Karaca
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
| | - Hanlong Chen
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Jingxi Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Deniz Mengu
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Mona Jarrahi
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Kaan Akşit
- University College London, Department of Computer Science, London, United Kingdom
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA.
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