Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processor

• URL: http://arxiv.org/abs/2401.08923v1
• Date: Wed, 17 Jan 2024 02:12:57 GMT
• Title: Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processor
• Authors: 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, and Aydogan Ozcan
• Abstract summary: We develop a compact, all-optical diffractive imager for subwavelength imaging of phase objects. The imager can find wide-ranging applications in bioimaging, endoscopy, sensing and materials characterization.
• Score: 9.47970290529295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Phase imaging is widely used in biomedical imaging, sensing, and material characterization, among other fields. However, direct imaging of phase objects with subwavelength resolution remains a challenge. Here, we demonstrate subwavelength imaging of phase and amplitude objects based on all-optical diffractive encoding and decoding. 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. The subsequent diffractive decoder layers (in air) are jointly designed with the encoder using deep-learning-based optimization, and communicate with the encoder layer to create magnified images of input objects at its output, revealing subwavelength features that would otherwise be washed away due to diffraction limit. We demonstrate that this all-optical collaboration between a diffractive solid-immersion encoder and the following decoder layers in air can resolve subwavelength phase and amplitude features of input objects in a highly compact design. To experimentally demonstrate its proof-of-concept, we used terahertz radiation and developed a fabrication method for creating monolithic multi-layer diffractive processors. Through these monolithically fabricated diffractive encoder-decoder pairs, we demonstrated phase-to-intensity transformations and all-optically reconstructed subwavelength phase features of input objects by directly transforming them into magnified intensity features at the output. This solid-immersion-based diffractive imager, with its compact and cost-effective design, can find wide-ranging applications in bioimaging, endoscopy, sensing and materials characterization.

Related papers

• A Hybrid Transformer-Mamba Network for Single Image Deraining [70.64069487982916]
  Existing deraining Transformers employ self-attention mechanisms with fixed-range windows or along channel dimensions. We introduce a novel dual-branch hybrid Transformer-Mamba network, denoted as TransMamba, aimed at effectively capturing long-range rain-related dependencies.
  arXiv  Detail & Related papers  (2024-08-31T10:03:19Z)
• Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor [11.382198495580127]
  We present a 3D stack of phase-only objects using a wavelength-multiplexed diffractive optical processor. We show that our diffractive processor could simultaneously achieve all-optical quantitative phase imaging across several distinct axial planes at the input.
  arXiv  Detail & Related papers  (2024-03-16T22:49:47Z)
• All-optical complex field imaging using diffractive processors [12.665552989073797]
  We present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields. Our design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field. 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.
  arXiv  Detail & Related papers  (2024-01-30T06:39:54Z)
• Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network [0.0]
  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. Our design utilizes spatially engineered diffractive layers, optimized through deep learning, to encode the phase profile of the input object. These diffractive multispectral processors maintain uniform performance across all the wavelength channels, revealing a decent QPI performance at each target wavelength.
  arXiv  Detail & Related papers  (2023-08-05T21:13:25Z)
• Shaping Single Photons through Multimode Optical Fibers using Mechanical Perturbations [55.41644538483948]
  We show an all-fiber approach for controlling the shape of single photons and the spatial correlations between entangled photon pairs. We optimize these perturbations to localize the spatial distribution of a single photon or the spatial correlations of photon pairs in a single spot.
  arXiv  Detail & Related papers  (2023-06-04T07:33:39Z)
• Super-resolution image display using diffractive decoders [21.24387597787123]
  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 deep learning-enabled diffractive display design that is based on a jointly-trained pair of an electronic encoder and a diffractive optical decoder. Our results indicate that this diffractive image display can achieve a super-resolution factor of 4, demonstrating a 16-fold increase in SBP.
  arXiv  Detail & Related papers  (2022-06-15T03:42:36Z)
• Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion [67.35540259040806]
  We propose a subpixel-level HS super-resolution framework by devising a novel decoupled-and-coupled network, called DCNet. As the name suggests, DC-Net first decouples the input into common (or cross-sensor) and sensor-specific components. We append a self-supervised learning module behind the CSU net by guaranteeing the material consistency to enhance the detailed appearances of the restored HS product.
  arXiv  Detail & Related papers  (2022-05-07T23:40:36Z)
• Diffractive all-optical computing for quantitative phase imaging [0.0]
  We demonstrate a diffractive QPI network that can synthesize the quantitative phase image of an object. A diffractive QPI network is a specialized all-optical processor designed to perform a quantitative phase-to-intensity transformation.
  arXiv  Detail & Related papers  (2022-01-22T05:28:44Z)
• Generative Modelling of BRDF Textures from Flash Images [50.660026124025265]
  We learn a latent space for easy capture, semantic editing, consistent, and efficient reproduction of visual material appearance. In a second step, conditioned on the material code, our method produces an infinite and diverse spatial field of BRDF model parameters.
  arXiv  Detail & Related papers  (2021-02-23T18:45:18Z)
• Modeling Lost Information in Lossy Image Compression [72.69327382643549]
  Lossy image compression is one of the most commonly used operators for digital images. We propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.
  arXiv  Detail & Related papers  (2020-06-22T04:04:56Z)
• Quantum metamaterial for nondestructive microwave photon counting [52.77024349608834]
  We introduce a single-photon detector design operating in the microwave domain based on a weakly nonlinear metamaterial. We show that the single-photon detection fidelity increases with the length of the metamaterial to approach one at experimentally realistic lengths. In stark contrast to conventional photon detectors operating in the optical domain, the photon is not destroyed by the detection and the photon wavepacket is minimally disturbed.
  arXiv  Detail & Related papers  (2020-05-13T18:00:03Z)

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.