Pyramid diffractive optical networks for unidirectional image magnification and demagnification

• URL: http://arxiv.org/abs/2308.15019v2
• Date: Wed, 31 Jul 2024 18:57:49 GMT
• Title: Pyramid diffractive optical networks for unidirectional image magnification and demagnification
• Authors: Bijie Bai, Xilin Yang, Tianyi Gan, Jingxi Li, Deniz Mengu, Mona Jarrahi, Aydogan Ozcan,
• Abstract summary: We present a pyramid-structured diffractive optical network design (which we term P-D2NN) for unidirectional image magnification and demagnification. The P-D2NN design creates high-fidelity magnified or demagnified images in only one direction, while inhibiting the image formation in the opposite direction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffractive deep neural networks (D2NNs) are composed of successive transmissive layers optimized using supervised deep learning to all-optically implement various computational tasks between an input and output field-of-view (FOV). Here, we present a pyramid-structured diffractive optical network design (which we term P-D2NN), optimized specifically for unidirectional image magnification and demagnification. In this design, the diffractive layers are pyramidally scaled in alignment with the direction of the image magnification or demagnification. This P-D2NN design creates high-fidelity magnified or demagnified images in only one direction, while inhibiting the image formation in the opposite direction - achieving the desired unidirectional imaging operation using a much smaller number of diffractive degrees of freedom within the optical processor volume. Furthermore, P-D2NN design maintains its unidirectional image magnification/demagnification functionality across a large band of illumination wavelengths despite being trained with a single wavelength. We also designed a wavelength-multiplexed P-D2NN, where a unidirectional magnifier and a unidirectional demagnifier operate simultaneously in opposite directions, at two distinct illumination wavelengths. Furthermore, we demonstrate that by cascading multiple unidirectional P-D2NN modules, we can achieve higher magnification factors. The efficacy of the P-D2NN architecture was also validated experimentally using terahertz illumination, successfully matching our numerical simulations. P-D2NN offers a physics-inspired strategy for designing task-specific visual processors.

Related papers

• Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks [0.0]
  As an optical processor, a Diffractive Deep Neural Network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing. We show that a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination.
  arXiv  Detail & Related papers  (2023-10-05T08:43:59Z)
• 2-D SSM: A General Spatial Layer for Visual Transformers [79.4957965474334]
  A central objective in computer vision is to design models with appropriate 2-D inductive bias. We leverage an expressive variation of the multidimensional State Space Model. Our approach introduces efficient parameterization, accelerated computation, and a suitable normalization scheme.
  arXiv  Detail & Related papers  (2023-06-11T09:41:37Z)
• Unidirectional Imaging using Deep Learning-Designed Materials [13.048762595058058]
  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. Here, we report the first demonstration of unidirectional imagers, presenting polarization-insensitive and broadband unidirectional imaging based on successive diffractive layers that are linear and isotropic. These diffractive layers are optimized using deep learning and consist of hundreds of thousands of diffractive phase features, which collectively modulate the incoming fields and project an intensity image of the input onto an output FOV, while blocking the image formation in the
  arXiv  Detail & Related papers  (2022-12-05T04:43:03Z)
• Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network [8.992945252617707]
  deep learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of transformations. Massively parallel, wavelength-multiplexed diffractive networks will be useful for designing high- throughput intelligent machine vision systems.
  arXiv  Detail & Related papers  (2022-08-13T07:59:39Z)
• All-optical graph representation learning using integrated diffractive photonic computing units [51.15389025760809]
  Photonic neural networks perform brain-inspired computations using photons instead of electrons. We propose an all-optical graph representation learning architecture, termed diffractive graph neural network (DGNN) We demonstrate the use of DGNN extracted features for node and graph-level classification tasks with benchmark databases and achieve superior performance.
  arXiv  Detail & Related papers  (2022-04-23T02:29:48Z)
• Disentangling Light Fields for Super-Resolution and Disparity Estimation [67.50796924758221]
  Light field (LF) cameras record both intensity and directions of light rays, and encode 3D scenes into 4D LF images. It is challenging for convolutional neural networks (CNNs) to process LF images since the spatial and angular information are highly inter-twined with varying disparities. We propose a generic mechanism to disentangle these coupled information for LF image processing.
  arXiv  Detail & Related papers  (2022-02-22T01:04:41Z)
• High-Resolution Optical Flow from 1D Attention and Correlation [89.61824964952949]
  We propose a new method for high-resolution optical flow estimation with significantly less computation. We first perform a 1D attention operation in the vertical direction of the target image, and then a simple 1D correlation in the horizontal direction of the attended image. Experiments on Sintel, KITTI and real-world 4K resolution images demonstrated the effectiveness and superiority of our proposed method.
  arXiv  Detail & Related papers  (2021-04-28T17:56:34Z)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)
• Learning Inter- and Intra-frame Representations for Non-Lambertian Photometric Stereo [14.5172791293107]
  We build a two-stage Convolutional Neural Network (CNN) architecture to construct inter- and intra-frame representations. We experimentally investigate numerous network design alternatives for identifying the optimal scheme to deploy inter-frame and intra-frame feature extraction modules.
  arXiv  Detail & Related papers  (2020-12-26T11:22:56Z)
• Deep learning-based virtual refocusing of images using an engineered point-spread function [1.2977570993112095]
  We present a virtual image refocusing method over an extended depth of field (DOF) enabled by cascaded neural networks and a double-helix point-spread function (DH-PSF) We extend the DOF of a fluorescence microscope by 20-fold.
  arXiv  Detail & Related papers  (2020-12-22T09:15:26Z)
• Higher-dimensional Hong-Ou-Mandel effect and state redistribution with linear-optical multiports [68.8204255655161]
  We expand the two-photon Hong-Ou-Mandel (HOM) effect onto a higher-dimensional set of spatial modes. We introduce an effect that allows controllable redistribution of quantum states over these modes using directionally unbiased linear-optical four-ports.
  arXiv  Detail & Related papers  (2020-12-16T04:50:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.