4.4 Article

Heralded phase-contrast imaging using an orbital angular momentum phase-filter

Journal

JOURNAL OF OPTICS
Volume 18, Issue 5, Pages -

Publisher

IOP PUBLISHING LTD
DOI: 10.1088/2040-8978/18/5/055204

Keywords

phase-contrast imaging; orbital angular momentum; ghost imaging

Categories

Funding

  1. UK EPSRC [EP/I012451/1]
  2. ERC through an Advanced Grant (TWISTS)
  3. Engineering and Physical Sciences Research Council [EP/I012451/1] Funding Source: researchfish
  4. EPSRC [EP/I012451/1] Funding Source: UKRI

Ask authors/readers for more resources

We utilise the position and orbital angular momentum (OAM) correlations between the signal and idler photons generated in the down-conversion process to obtain ghost images of a phase object. By using an OAM phase filter, which is non-local with respect to the object, the images exhibit isotropic edge-enhancement. This imaging technique is the first demonstration of a full-field, phase-contrast imaging system with non-local edge enhancement, and enables imaging of phase objects using significantly fewer photons than standard phase-contrast imaging techniques.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.4
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Computer Science, Artificial Intelligence

Infrared target tracking based on proximal robust principal component analysis method

Chao Ma, Minjie Wan, Yunkai Xu, Kan Ren, Weixian Qian, Qian Chen, Guohua Gu

Summary: This paper proposes an infrared target tracker based on the proximal robust principal component analysis method, solves the convex optimization problem using the Alternating Direction Method of Multipliers, and locates the target using the particle filter framework.

APPLIED INTELLIGENCE (2022)

Article Computer Science, Artificial Intelligence

Infrared small target detection via region super resolution generative adversarial network

Kan Ren, Yuan Gao, Minjie Wan, Guohua Gu, Qian Chen

Summary: Infrared small target detection has always been a challenging problem due to the limited pixels and features of small infrared targets. Current optimization methods primarily focus on multi-scale feature fusion or super-resolution enhancement. However, when applying super-resolution networks to infrared target detection, two significant issues arise: excessive computational power consumption, resulting in low detection rates, and the disparity between low-resolution training images and the actual distribution of tiny targets, leading to poor detection accuracy. This paper proposes a new detection network, RSRGAN, which consists of a computationally efficient backbone network (RCN) for extracting potential regions and a GAN-based generator that includes modules for distribution transformation and super-resolution enhancement. The discriminator assists in generating better super-resolution images by distinguishing between generated and actual images. Additionally, the authors create an infrared UAV small target dataset, which includes birds, leaves, and other disturbances, to evaluate the algorithm's performance. Experimental results demonstrate that the proposed method achieves better detection of small IR targets and outperforms existing approaches.

APPLIED INTELLIGENCE (2022)

Article Computer Science, Artificial Intelligence

Object matching between visible and infrared images using a Siamese network

Wuxin Li, Qian Chen, Guohua Gu, Xiubao Sui

Summary: The study proposes a method for object matching between visible and infrared images using a Siamese neural network combined with a convolutional neural network for feature extraction and cross-correlation calculations for matched targets, achieving higher accuracy and precision in experiments.

APPLIED INTELLIGENCE (2022)

Article Geochemistry & Geophysics

Total Variation-Based Interframe Infrared Patch-Image Model for Small Target Detection

Minjie Wan, Guohua Gu, Yunkai Xu, Weixian Qian, Kan Ren, Qian Chen

Summary: This paper proposes a total variation-based interframe infrared patch-image model for infrared small target detection. By converting the infrared image into a patch-image consisting of a sparse target matrix and a low-rank background matrix, and introducing temporal consistency constraint and TV regularization term, the proposed model achieves better performance in infrared small target detection.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

Article Optics

Composite fringe projection deep learning profilometry for single-shot absolute 3D shape measurement

Yixuan Li, Jiaming Qian, Shijie Feng, Qian Chen, Chao Zuo

Summary: Single-shot fringe projection profilometry (FPP) is crucial for retrieving the absolute depth information of objects in high-speed dynamic scenes. This study proposes a composite fringe projection deep learning profilometry (CDLP) method that combines deep learning and physical model to achieve high-precision and unambiguous phase retrieval on a single composite fringe image. The method overcomes the problem of serious spectrum aliasing caused by multiplexing schemes and can reconstruct high-quality absolute 3D surfaces.

OPTICS EXPRESS (2022)

Article Optics

Single-exposure 3D label-free microscopy based on color-multiplexed intensity diffraction tomography

Ning Zhou, Jiaji Li, Runnan Zhang, Zhidong Bai, Shun Zhou, Qian Chen, Chao Zuo

Summary: This study presents a 3D label-free refractive index imaging technique based on single-exposure intensity diffraction tomography (sIDT) using a color-multiplexed illumination scheme. By capturing the scattering field from different directions and separating monochromatic intensity images, the 3D refractive index distribution of the object can be reconstructed. The method demonstrates reliable performance in label-free, high-throughput, and real-time 3D volumetric biological imaging applications.

OPTICS LETTERS (2022)

Article Chemistry, Multidisciplinary

Point Cloud Segmentation from iPhone-Based LiDAR Sensors Using the Tensor Feature

Xuan Wang, Haiyang Lyu, Tianyi Mao, Weiji He, Qian Chen

Summary: This paper proposes a method for point cloud segmentation using tensor feature, which can solve the difficulty of segmenting point cloud data obtained from unprofessional LiDAR devices.

APPLIED SCIENCES-BASEL (2022)

Correction Optics

Deep learning in optical metrology: a review (vol 11, 39, 2022)

Chao Zuo, Jiaming Qian, Shijie Feng, Wei Yin, Yixuan Li, Pengfei Fan, Jing Han, Kemao Qian, Qian Chen

LIGHT-SCIENCE & APPLICATIONS (2022)

Review Optics

Deep learning in optical metrology: a review

Chao Zuo, Jiaming Qian, Shijie Feng, Wei Yin, Yixuan Li, Pengfei Fan, Jing Han, Kemao Qian, Qian Chen

Summary: Optical metrology, with the advances in deep learning technologies, has become a versatile problem-solving tool in various fields such as manufacturing, fundamental research, and engineering applications. This review provides an overview of the current status and latest progress of deep learning in optical metrology, covering applications in tasks like fringe denoising, phase retrieval, and error compensation. The challenges and future research directions are also discussed.

LIGHT-SCIENCE & APPLICATIONS (2022)

Article Optics

Infrared Ocean Image Simulation Algorithm Based on Pierson-Moskowitz Spectrum and Bidirectional Reflectance Distribution Function

Xueqi Chen, Lin Zhou, Meng Zhou, Ajun Shao, Kan Ren, Qian Chen, Guohua Gu, Minjie Wan

Summary: This paper proposes an infrared ocean image simulation algorithm based on the Pierson-Moskowitz spectrum and a bidirectional reflectance distribution function, which can provide more authentic and clear simulation images.

PHOTONICS (2022)

Article Optics

Intensity-guided depth image estimation in long-range lidar

Miao Wu, Yu Lu, Haochen Li, Tianyi Mao, Yanqiu Guan, Labao Zhang, Weiji He, Peiheng Wu, Qian Chen

Summary: Long-range lidar systems often record large but extremely sparse data cubes, making it challenging to accurately estimate depth images. This paper introduces an intensity-guided method that utilizes temporal-spatial correlation to estimate depth images. Preprocessing steps are used to reduce the data size. Experimental results show that this method outperforms other state-of-the-art methods in estimating depth images, particularly in low signal return scenarios.

OPTICS AND LASERS IN ENGINEERING (2022)

Article Optics

Hiding images in noise

Steven Johnson, Alex McMillan, Stefan Frick, John Rarity, Miles Padgett

Summary: A limitation of free-space optical communications is the ease of interception, which can be overcome by hiding information within background optical noise. We demonstrate image transfer over free-space using a photon-pair source emitting two correlated beams. One beam contains image information with added noise, while the other correlated beam serves as a heralding trigger to differentiate the image signal from background noise. The system utilizes spontaneous parametric down-conversion and a gated intensified camera to extract the image from the noise.

OPTICS EXPRESS (2023)

Article Optics

Roadmap on structured waves

Konstantin Y. Bliokh, Ebrahim Karimi, Miles J. Padgett, Miguel A. Alonso, Mark R. Dennis, Angela Dudley, Andrew Forbes, Sina Zahedpour, Scott W. Hancock, Howard M. Milchberg, Stefan Rotter, Franco Nori, Sahin K. Ozdemir, Nicholas Bender, Hui Cao, Paul B. Corkum, Carlos Hernandez-Garcia, Haoran Ren, Yuri Kivshar, Mario G. Silveirinha, Nader Engheta, Arno Rauschenbeutel, Philipp Schneeweiss, Juergen Volz, Daniel Leykam, Daria A. Smirnova, Kexiu Rong, Bo Wang, Erez Hasman, Michela F. Picardi, Anatoly Zayats, Francisco J. Rodriguez-Fortuno, Chenwen Yang, Jie Ren, Alexander B. Khanikaev, Andrea Alu, Etienne Brasselet, Michael Shats, Jo Verbeeck, Peter Schattschneider, Dusan Sarenac, David G. Cory, Dmitry A. Pushin, Michael Birk, Alexey Gorlach, Ido Kaminer, Filippo Cardano, Lorenzo Marrucci, Mario Krenn, Florian Marquardt

Summary: Structured waves are found in all areas of wave physics, both classical and quantum, where the wavefields are inhomogeneous and cannot be approximated by a single plane wave. These complex wavefields with inhomogeneities are crucial in various fields such as nanooptics, photonics, quantum matter waves, acoustics, water waves, etc. This Roadmap surveys the role of structured waves in wave physics, providing background, current research, and anticipating future developments.

JOURNAL OF OPTICS (2023)

Article Optics

Measuring optical activity with unpolarized light: Ghost polarimetry

Sara Restuccia, Graham M. Gibson, Leroy Cronin, Miles J. Padgett

Summary: This study demonstrates the measurement of optical activity in a sample using an unpolarized light source, with the help of a polarization-entangled photon source. This approach allows for low light measurement and the analysis of samples that may be perturbed by polarized light.

PHYSICAL REVIEW A (2022)

Article Optics

Single-pixel imaging with heralded single photons

Steven Johnson, Alex McMillan, Yril Torre, Stefan Frick, John Rarity, Miles Padgett

Summary: Traditional remote sensing applications based on pulsed laser illumination are not suitable for covert operation. We present a method that uses correlated photon-pairs to perform single-pixel imaging, suppressing background light effect and improving signal-to-noise ratio.

OPTICS CONTINUUM (2022)

No Data Available