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Self-supervised model-informed deep learning for low-SNR SS-OCT domain transformation

Scientific reports 2025年第1期15卷 17791页

作者： Sajed Rakhshani Amirali Arbab Aref Habibi Mohsen Pourazizi Mahnoosh Tajmirriahi Farnaz Sedighin Hossein Rabbani Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran. Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran. Department of Ophthalmology Isfahan Eye Research Center Isfahan University of Medical Sciences Isfahan Iran. Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran. rabbani.h@***.

This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (SDNet), for simultaneous denoising and super-resolution of swept-source optical coherence tomography (SS-OCT) images. The novelty of this work lies in the hybrid integration of data-driven deep-learning with a model-informed noise representation, specifically designed to address the very low signal-to-noise ratio (SNR) and low-resolution challenges in SS-OCT imaging. SDNet introduces a two-step training process, leveraging noise-free OCT references to simulate low-SNR conditions. In the first step, the network learns to enhance noisy images by combining denoising and super-resolution within noise-corrupted reference domain. To refine its performance, the second step incorporates Principle Component Analysis (PCA) as self-supervised denoising strategy, eliminating the need for ground-truth noisy image data. This unique approach enhances SDNet's adaptability and clinical relevance. A key advantage of SDNet is its ability to balance contrast-texture by adjusting the weights of the two training steps, offering clinicians flexibility for specific diagnostic needs. Experimental results across diverse datasets demonstrate that SDNet surpasses traditional model-based and data-driven methods in computational efficiency, noise reduction, and structural fidelity. The framework excels in improving both image quality and diagnostic accuracy. Additionally, SDNet shows promising adaptability for analyzing low-resolution, low-SNR OCT images, such as those from patients with diabetic macular edema (DME). This study establishes SDNet as a robust, efficient, and clinically adaptable solution for OCT image enhancement addressing critical limitations in contemporary imaging workflows.

关键词： Denoising Low-SNR OCT Model-aware deep learning Self-supervised learning Super-resolution

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Automatic Modulation Classification of Frequency-Hopping signals Using High-Dimensional Phase Diagrams

Automatic Modulation Classification of Frequency-Hopping Sig...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Qibo Chen Kun Yan Hsiao-Chun Wu Shih Yu Chang Xiao Yan Yiyan Wu Haonan Chang Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin Guangxi China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Department of Applied Data Science San Jose State University San Jose CA USA School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu P. R. China Communications Research Centre Canada Ottawa Canada Haonan Chang is with Xi'an University Xi'an P. R. China

Automatic modulation recognition for frequency-hopping (FH) signals remains very challenging to researchers due to the signals' time-varying spectral characteristics. In this work, a novel robust automatic modulation recognition scheme is investigated for FH signals using the phase-space topological features represented by the embedded phase diagrams. As such embedded phase diagrams are often high-dimensional, it is necessary to formulate the phase-space features as tensors. In the training process, the phase-space tensor features will be utilized to establish the regression models as linear encoders for the individual modulations. The aforementioned linear encoders are constructed using the support vector machine (SVM); the phase-space feature-tensors of the training signals of all modulations will be projected by their corresponding regression models (or linearly encoded) to produce the representative code-vectors, respectively. In the test stage, the phase-space feature-tensor produced from a test signal will be projected by each individual trained regression model (or linearly encoded) to generate the respective code-vectors. Then, the code-vectors resulting from the test stage will be compared with the representative code-vectors to find which modulation will lead to the smallest Euclidean distance in between and such a modulation will be picked as the modulation type of the test signal. Monte Carlo simulation results have demonstrated that the average recognition accuracy of our proposed new approach is more than 90% when the signal-to-noise ratio is no less than 0 dB for additive white Gaussian noise.

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Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN

arXiv

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arXiv 2022年

作者： Mo, Yaozong Li, Chaofeng Ren, Wenqi Shang, Shaopeng Wang, Wenwu Wu, Xiao-Jun The Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai201306 China The School of Cyber Science and Technology Sun Yatsen University Shenzhen518000 China The Vocational College Shanghai Jian Qiao University Shanghai201306 China The Center for Vision Speech and Signal Processing Department of Electrical and Electronic Engineering University of Surrey SurreyGU2 7XH United Kingdom The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

Deep learning-based methods have achieved significant performance for image defogging. However, existing methods are mainly developed for land scenes and perform poorly when dealing with overwater foggy images, since overwater scenes typically contain large expanses of sky and water. In this work, we propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. To promote the recovery of the objects on water in the image, two loss functions are exploited for the network where a prior map is designed to invert the dark channel and the min-max normalization is used to suppress the sky and emphasize objects. However, due to the unpaired training set, the network may learn an under-constrained domain mapping from foggy to fog-free image, leading to artifacts and loss of details. Thus, we propose an intuitive Upscaling Inception Module (UIM) and a Long-range Residual Coarse-to-fine framework (LRC) to mitigate this issue. Extensive experiments on qualitative and quantitative comparisons demonstrate that the proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Optimum elements selection in IRS-assisted UAV communications with phase errors

TechRxiv

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TechRxiv 2021年

作者： Jangsher, Sobia Al-Dweik, Arafat Al-Jarrah, Mohammd Ahmad Alusa, Emad Alouini, Mohamed-Slim Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Wireless and Signal Processing Lab Institute of Space Technology Islamabad Pakistan School of Electrical and Electronic Engineering University of manchester ManchesterM13 9PL United Kingdom Center for Cyber Physical Systems Khalifa University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering Western University LondonON Canada Thuwal Makkah Province Saudi Arabia

This letter considers minimizing the bit error rate (BER) of unmanned aerial vehicle (UAV) communications assisted by intelligent reflecting surfaces (IRSs). By noting that increasing the number of IRS elements in the presence of phase errors does not necessarily improve the BER, it is crucial to use only the elements that contribute to reducing the BER. Consequently, we propose an efficient algorithm to activate only the elements that improve the BER. The proposed algorithm has lower complexity and comparable BER to the optimum selection process, which is an NP-hard problem. The accuracy of the estimated phase is evaluated by deriving the probability distribution function (PDF) of the least-square (LS) channel estimator, and showing that the PDF can be closely approximated by the von Mises distribution at high signal-to-noise ratios (SNRs). The obtained analytical and simulation results show that using all the available reflectors can significantly deteriorate the BER, and thus, elements’ selection is necessary. In particular scenarios, using about 26% of the reflectors provides more than 10 fold BER reduction. © 2021, CC BY.

关键词： Bit error rate

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A conditional one-output likelihood formulation for multitask Gaussian processes

arXiv

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arXiv 2020年

作者： García-Hinde, Óscar Gómez-Verdejo, Vanessa Martínez-Ramón, Manel Department of Signal Processing and Communications Universidad Carlos III de Madrid Leganés 28911 Spain Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM8711 United States

Multitask Gaussian processes (MTGP) are the Gaussian process (GP) framework’s solution for multioutput regression problems in which the T elements of the regressors cannot be considered conditionally independent given the observations. Standard MTGP models assume that there exist both a multitask covariance matrix as a function of an intertask matrix, and a noise covariance matrix. These matrices need to be approximated by a low rank simplification of order P in order to reduce the number of parameters to be learnt from T2 to TP. Here we introduce a novel approach that simplifies the multitask learning by reducing it to a set of conditioned univariate GPs without the need for any low rank approximations, therefore completely eliminating the requirement to select an adequate value for hyperparameter P. At the same time, by extending this approach with both a hierarchical and an approximate model, the proposed extensions are capable of recovering the multitask covariance and noise matrices after learning only 2T parameters, avoiding the validation of any model hyperparameter and reducing the overall complexity of the model as well as the risk of overfitting. Experimental results over synthetic and real problems confirm the advantages of this inference approach in its ability to accurately recover the original noise and signal matrices, as well as the achieved performance improvement in comparison to other state of art MTGP approaches. We have also integrated the model with standard GP toolboxes, showing that it is computationally competitive with state of the art options. © 2020, CC BY-NC-ND.

关键词： Covariance matrix

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Target detection and segmentation in circular-scan synthetic-aperture-sonar images using semi-supervised convolutional encoder-decoders

arXiv

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arXiv 2021年

作者： Sledge, Isaac J. Emigh, Matthew S. King, Jonathan L. Woods, Denton L. Tory Cobb, J. Príncipe, José C. The Advanced Signal Processing and Automated Target Recognition Branch Naval Surface Warfare Center Panama CityFL32407 United States The Applied Sensing and Processing Branch Naval Surface Warfare Center Panama CityFL32407 United States The Littoral Acoustics and Target Physics Branch Naval Surface Warfare Center Panama CityFL32407 United States The Department of Electrical and Computer Engineering The Department of Biomedical Engineering University of Florida GainesvilleFL32611 United States

We propose a framework for saliency-based, multi-target detection and segmentation of circular-scan, synthetic-aperture-sonar (CSAS) imagery. Our framework relies on a multi-branch, convolutional encoder-decoder network (MB-CEDN). The encoder portion of the MB-CEDN extracts visual contrast features from CSAS images. These features are fed into dual decoders that perform pixel-level segmentation to mask targets. Each decoder provides different perspectives as to what constitutes a salient target. These opinions are aggregated and cascaded into a deep-parsing network to refine the segmentation. We evaluate our framework using real-world CSAS imagery consisting of five broad target classes. We compare against existing approaches from the computer-vision literature. We show that our framework outperforms supervised, deep-saliency networks designed for natural imagery. It greatly outperforms unsupervised saliency approaches developed for natural imagery. This illustrates that natural-image-based models may need to be altered to be effective for this imaging-sonar modality. © 2021, CC BY.

关键词： Sonar

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Fully Passive Quantum Key Distribution

引用

Physical Review Letters 2023年第22期130卷 220801-220801页

作者： Wenyuan Wang Rong Wang Chengqiu Hu Victor Zapatero Li Qian Bing Qi Marcos Curty Hoi-Kwong Lo Department of Physics University of Hong Kong Pokfulam Road Hong Kong Vigo Quantum Communication Center University of Vigo Vigo E-36310 Spain Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo Vigo E-36310 Spain AtlanTTic Research Center University of Vigo Vigo E-36310 Spain Department of Electrical and Computer Engineering University of Toronto Toronto Ontario M5S 3G4 Canada Centre for Quantum Information and Quantum Control (CQIQC) University of Toronto Toronto Ontario M5S 1A7 Canada Cisco Systems San Jose California 95134 USA Department of Physics University of Toronto Toronto Ontario M5S 1A7 Canada Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada

We propose a fully passive linear optical quantum key distribution (QKD) source that implements both random decoy-state and encoding choices with postselection only, thus eliminating all side channels in active modulators. Our source is general purpose and can be used in, e.g., BB84, the six-state protocol, and reference-frame-independent QKD. It can even potentially be combined with measurement-device-independent QKD to achieve robustness against side channels in both detectors and modulators. We also perform a proof-of-principle experimental source characterization to show its feasibility.

关键词： Optical quantum information processing Quantum communication Quantum cryptography Quantum optics Quantum protocols

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Optical MIMO Transmission focusing on photonic lanterns and optical couplers 1

引用

14th International Joint Conference on e-Business and Telecommunications, ICETE 2017

作者： Ahrens, Andreas Sandmann, André Lochmann, Steffen Department of Electrical Engineering and Computer Science Communications Signal Processing Group Hochschule Wismar University of Applied Sciences: Technology Business and Design Philipp-Müller-Straße 14 Wismar23966 Germany

ISBN: (数字)9783030110390

ISBN: (纸本)9783030110383

The exploitation of the spatial domain with the concept of multiple-input multiple-output (MIMO) is a promising approach to further improve the cost efficiency in fiber-optic communications. Historically, the first optical MIMO systems utilized multi-mode couplers for the purpose of mode multiplexing (MUX) and demultiplexing (DEMUX). However, their high insertion losses and asymmetries demand for alternative components. Nowadays, next to optical couplers, photonic lanterns have become considerably more attractive offering low insertion losses and being able to excite individual modes. Therefore, they are in the focus of this contribution. A setup of 6-port photonic lanterns is evaluated by measurements and compared with other multiplexing components. The measurement results and the simulated bit-error rate performances highlight that photonic lanterns are well-suited for optical MIMO communications. © Springer Nature Switzerland AG 2019.

关键词： Insertion losses

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Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network

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PLoS Computational Biology 2021年第9期17卷 e1009345-e1009345页

作者： Zhao, Zhengqiao Woloszynek, Stephen Agbavor, Felix Mell, Joshua Chang Sokhansanj, Bahrad A. Rosen, Gail L. Ecological and Evolutionary Signal-Processing and Informatics Laboratory Department of Electrical and Computer Engineering College of Engineering Drexel University PhiladelphiaPA United States Beth Israel Deaconess Medical Center BostonMA United States Harvard Medical School BostonMA United States School of Biomedical Engineering Science and Health Systems Drexel University PhiladelphiaPA United States College of Medicine Drexel University PhiladelphiaPA United States

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/ sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. We demonstrate that our implementation of a novel attention- based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://***/EESI/sequence_attention (a python package) and https://***/ EESI/seq2att (a command line tool). Copyright: © 2021 Zhao et

关键词： Classification (of information)

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Fully-Passive Quantum Key Distribution

arXiv

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arXiv 2022年

作者： Wang, Wenyuan Wang, Rong Zapatero, Victor Qian, Li Qi, Bing Curty, Marcos Lo, Hoi-Kwong Department of Physics University of Hong Kong Pokfulam Road Hong Kong Escuela de Ingeniería de Telecomunicación Department of Signal Theory and Communications University of Vigo Vigo Spain atlanTTic Research Center University of Vigo VigoE-36310 Spain Dept. of Electrical & Computer Engineering University of Toronto TorontoONM5S 3G4 Canada Cisco Systems San JoseCA95134 United States Dept. of Physics University of Toronto TorontoONM5S 3G4 Canada

Passive implementations of quantum key distribution (QKD) sources are highly desirable as they eliminate side-channels that active modulators might introduce. Up till now, passive decoy-state and passive encoding BB84 schemes have both been proposed. Nonetheless, passive decoy-state generation and passive encoding have never been simultaneously implemented with linear optical elements before, which greatly limits the practicality of such passive QKD schemes. In this work, we overcome this limitation and propose a fully-passive QKD source with linear optics that eliminates active modulators for both decoy-state choice and encoding. This allows for highly practical QKD systems that avoid side-channels from the source modulators. The passive source we propose (combined with the decoy-state analysis) can create any arbitrary state on a qubit system and is protocol-independent. That is, it can be used for various protocols such as BB84, reference-frame-independent QKD, or the six-state protocol. It can also in principle be combined with e.g. measurement-device-independent QKD, to build a system without side-channels in either detectors or modulators. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum cryptography

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