Broad-spectrum diffractive network via ensemble learning

Chen, Yingshi; Zhang, Xinyu

doi:10.1364/ol.440421

Cited by 19 publications

(10 citation statements)

References 16 publications

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“…To create an all-optical QPI solution without any digital phase reconstruction algorithm, we designed diffractive networks [54][55][56][57][58] that transform the phase information of the input sample into an output intensity pattern, quantitatively revealing the object phase distribution through an intensity recording. Figure 1 illustrates the schematic of a 5-layer diffractive network that was trained to all-optically synthesize the QPI signal of a given input phase object (see the Experimental Section for training details).…”

Section: Resultsmentioning

confidence: 99%

All‐Optical Phase Recovery: Diffractive Computing for Quantitative Phase Imaging

Mengü

Ozcan

2022

Advanced Optical Materials

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Quantitative phase imaging (QPI) is a label‐free computational imaging technique that provides optical path length information of specimens. In modern implementations, the quantitative phase image of an object is reconstructed digitally through numerical methods running in a computer, often using iterative algorithms. Here, a diffractive QPI network that can perform all‐optical phase recovery is demonstrated, and the quantitative phase image of an object is synthesized by converting the input phase information of a scene into intensity variations at the output plane. A diffractive QPI network is a specialized all‐optical processor designed to perform a quantitative phase‐to‐intensity transformation through passive diffractive surfaces that are spatially engineered using deep learning and image data. Forming a compact, all‐optical network that axially extends only ≈200–300λ, where λ is the illumination wavelength, this framework can replace traditional QPI systems and related digital computational burden with a set of passive transmissive layers. All‐optical diffractive QPI networks can potentially enable power‐efficient, high frame‐rate, and compact phase imaging systems that might be useful for various applications, including, e.g., microscopy and sensing.

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Section: Resultsmentioning

confidence: 99%

All‐Optical Phase Recovery: Diffractive Computing for Quantitative Phase Imaging

Mengü

Ozcan

2022

Advanced Optical Materials

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“…In response, researchers have embraced the potential of lightwave technology, harnessing its attributes of remarkable speed, wide bandwidth, and minimal crosstalk. Optical neural networks [1][2][3][4][5][6][7], encompassing systems based on silicon photonics and diffractive surfaces, have emerged as promising avenues for achieving rapid object detection, as well as facilitating efficient backpropagation computation and beam steering.…”

Section: Introductionmentioning

confidence: 99%

Robust diffractive neural networks for resilient all-optical intelligent imaging detection

shi,

zhang

2024

MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications

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Conventional imaging detection relies on electrical modulation, but it grapples with limitations in speed and adaptability within complex environments. Intelligent imaging detection, capable of manipulating micro-nano-lightfields generated by imaging objective lenses, is essential. The all-optical deep diffractive neural network (D2NN) offers a potential solution, enabling swift spatial information transformation and recognition. However, existing D2NN versions are vulnerable to optical disturbances due to their reliance on computer-based backpropagation. This paper introduces a robust diffractive neural network (SRNN) mathematical model designed to mitigate the impact of structural errors and lightwave frequency shifting without necessitating hardware modifications. The SRNN model incorporates Gaussian noise at various levels during training to emulate the error distribution of the phase mask, augmenting the network's resilience to hardware errors. Weight-Noise-Injection training assists the network in locating an error-insensitive region, thereby enhancing its robustness.

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“…Diffractive networks can achieve universal linear transformations, [ 7–9 ] and various applications using diffractive processors have been demonstrated such as object classification, pulse processing, imaging through random diffusers, hologram reconstruction, quantitative phase imaging, class‐specific imaging, super‐resolution image display, all‐optical logic operations, beam shaping, and orbital angular momentum mode processing, among others. [ 10–30 ]…”

Section: Introductionmentioning

confidence: 99%

Time‐Lapse Image Classification Using a Diffractive Neural Network

Rahman

Ozcan

2023

Advanced Intelligent Systems

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Machine learning and artificial intelligence research has experienced rapid growth in the last two decades. [1] One of the core engines that has driven this growth is deep learning, [2] permitting efficient and rapid training of deep artificial neural network models. The ability to train deep neural networks has revolutionized artificial intelligence, and electronics has been the undisputed platform of choice for implementing artificial neural networks. Specialized processing hardware such as graphics processing unit (GPU) is widely used today for deep learning. However, these electronic processors are powerhungry and bulky, making researchers wary of the environmental impact of machine learning. [3,4] Therefore, there is strong interest in low-power and fast computing platforms for machine learning applications. Optical computing has been identified as a promising potential alternative for such purposes because of the large bandwidth, high speed, and massive parallelism of optics. [5] Diffractive deep neural networks (D 2 NNs), also known as diffractive optical networks or diffractive networks, form a passive all-optical computing platform that exploits the diffraction of light waves to perform computation. [6] These diffractive networks are composed of several spatially engineered surfaces, separated by free-space. The diffractive features/elements of a layer, also termed "diffractive neurons", locally modulate the amplitude and/or the phase of the light incident upon the layer. Successive modulation by and diffraction through the layers give rise to an all-optical transformation between the input and the output fields-of-view at the speed of light propagation without any external power. The amplitude and/or the phase values of the diffractive neurons corresponding to a desired optical transformation or computational task are trained/learned through a digital computer using deep learning. Once the training is complete, the layers can be fabricated and assembled to form a "physical" network that performs the desired computation in a passive manner and at the speed of light propagation. Diffractive networks can achieve universal linear transformations, [7][8][9] and various applications using diffractive processors have been demonstrated such as object classification, pulse processing, imaging through random diffusers, hologram reconstruction, quantitative phase imaging, class-specific imaging, super-resolution image display, all-optical logic

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Broad-spectrum diffractive network via ensemble learning

Cited by 19 publications

References 16 publications

All‐Optical Phase Recovery: Diffractive Computing for Quantitative Phase Imaging

All‐Optical Phase Recovery: Diffractive Computing for Quantitative Phase Imaging

Robust diffractive neural networks for resilient all-optical intelligent imaging detection

Time‐Lapse Image Classification Using a Diffractive Neural Network

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