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Passive Amplification and Noise Mitigation of Optical signals Through Talbot processing

JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第3期41卷 797-814页

作者： Crockett, Benjamin Cortes, Luis Romero Azana, Jose Inst Natl Rech Sci Ctr Energie Mat Telecommun INRS EMT Varennes PQ J3X 1P7 Canada Univ Politecn Valencia Photon Res Labs Valencia 46022 Spain

Noise is one of the rare aspects of experimental work that crosses all boundaries. It is present from scientific fields like ultrafast optical signal detection to applied fields such as image processing, or even in our day-to-day lives when we are simply trying to have a conversation in a loud room. In all these cases, incoherent, stochastic noise tends to drown a signal we aim to detect, and various techniques may need to be employed to improve the clarity of the waveform, which is characterized by the signal-to-noise ratio (SNR). Yet, considering the ubiquity of noise in scientific and technology fields, it may be surprising how few methods there exists for denoising a signal. Active amplification techniques alone cannot be employed for weak, noisy signals, since the SNR is inevitably degraded due to fundamental laws of physics, while bandpass filtering schemes necessarily lead to an attenuation of the signal. In this article, we review recent advances on the concept of passive amplification techniques based on the Talbot effect to enhance the noise properties of signals through coherent energy redistribution. We demonstrate the basic framework starting from pulse repetition rate multiplication with the Talbot effect. We then extend this theory to show the principle behind passive amplification of periodic waveforms, and then how this idea can be extended to arbitrary (generally, aperiodic) signals. methods for passive amplification of both the time-domain and the frequency-domain representations of the signal of interest are reviewed. While here we focus on the application of the technique for optical signals in the standard telecommunication band (near wavelengths of 1550 nm), the proposed denoising scheme relies on widely available wave manipulations, such that it may offer exciting opportunities for any kind of physical wave support, such as acoustics, plasmonics and other regimes of the electromagnetic spectrum, like microwaves or X-rays.

关键词： Narrowband Ultrafast optics Stimulated emission Optical noise Optical fiber amplifiers Filtering Optical signal processing Linear optics noise mitigation signal processing signal restoration Talbot effect

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Hardware acceleration of YOLOv7-tiny using high-level synthesis tools

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JOURNAL OF REAL-TIME image processing 2023年第4期20卷 1-10页

作者： Hosseiny, Adib Jahanirad, Hadi Univ Kurdistan Dept Elect & Commun Engn Sanandaj 90210 Iran

FPGAs have emerged as a promising platform for implementing neural networks due to their reconfigurability, parallelism, and low power consumption. Nonetheless, designing and optimizing FPGA-based neural network accelerators is a complex and time-consuming task with register transfer level (RTL) languages. High-level synthesis (HLS) tools provide a higher level of abstraction for FPGA design, enabling designers to concentrate on top-level design aspects, such as algorithms, rather than low-level hardware implementation details. One of the state-of-the-art object detection networks is you look only once (YOLO) network series which is constructed using different neural network technologies using cross-stage connections and feature extraction techniques like pyramid networks. In this paper, we propose a method for the implementation of YOLOv7-tiny network on FPGAs using HLS tools. We present a comprehensive analysis of the performance and resource utilization of FPGA-based neural network accelerators. Our methods show excellent results for real-time application requirements such as latency. Specifically, our work reduces the usage of digital signal processing (DSP) units by 90% and it saves up to 60% of flip-flops compared to state-of-the-art designs, while achieving competitive usage of block RAM and look-up tables. Additionally, the achieved design latency of 15 ms is extremely suitable for real-time applications. Also we will propose a method for BRAM utilization method and off-chip memory access.

关键词： High level synthesis Convolutional neural network Object detection FPGA YOLO

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Self-Supervised Spontaneous Latent-Based Facial Expression Sequence Generation

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2023年 4卷 304-312页

作者： Yap, Chuin Hong Yap, Moi Hoon Davison, Adrian K. Cunningham, Ryan Manchester Metropolitan Univ Dept Comp & Math Manchester M15 6BH England

In this article, we investigate the spontaneity issue in facial expression sequence generation. Current leading methods in the field are commonly reliant on manually adjusted conditional variables to direct the model to generate a specific class of expression. We propose a neural network-based method which uses Gaussian noise to model spontaneity in the generation process, removing the need for manual control of conditional generation variables. Our model takes two sequential images as input, with additive noise, and produces the next image in the sequence. We trained two types of models: single-expression, and mixed-expression. With single-expression, unique facial movements of certain emotion class can be generated;with mixed expressions, fully spontaneous expression sequence generation can be achieved. We compared our method to current leading generation methods on a variety of publicly available datasets. Initial qualitative results show our method produces visually more realistic expressions and facial action unit (AU) trajectories;initial quantitative results using image quality metrics (SSIM and NIQE) show the quality of our generated images is higher. Our approach and results are novel in the field of facial expression generation, with potential wider applications to other sequence generation tasks.

关键词： Faces Mathematical models Markov processes Gold Training Task analysis image sequences Affective computing artificial neural networks self-supervised learning

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Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid fitness function

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neural COMPUTING & APPLICATIONS 2023年第1期35卷 855-886页

作者： Hosny, Khalid M. Khalid, Asmaa M. Hamza, Hanaa M. Mirjalili, Seyedali Zagazig Univ Fac Comp & Informat Dept Informat Technol Zagazig 44519 Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia

image segmentation is a critical step in digital image processing applications. One of the most preferred methods for image segmentation is multilevel thresholding, in which a set of threshold values is determined to divide an image into different classes. However, the computational complexity increases when the required thresholds are high. Therefore, this paper introduces a modified Coronavirus Optimization algorithm for image segmentation. In the proposed algorithm, the chaotic map concept is added to the initialization step of the naive algorithm to increase the diversity of solutions. A hybrid of the two commonly used methods, Otsu's and Kapur's entropy, is applied to form a new fitness function to determine the optimum threshold values. The proposed algorithm is evaluated using two different datasets, including six benchmarks and six satellite images. Various evaluation metrics are used to measure the quality of the segmented images using the proposed algorithm, such as mean square error, peak signal-to-noise ratio, Structural Similarity Index, Feature Similarity Index, and Normalized Correlation Coefficient. Additionally, the best fitness values are calculated to demonstrate the proposed method's ability to find the optimum solution. The obtained results are compared to eleven powerful and recent metaheuristics and prove the superiority of the proposed algorithm in the image segmentation problem.

关键词： image segmentation Optimization Thresholding Metaheuristic Satellite

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Extreme Low Bitrate image Compression System for Mobile Deployment 26

Extreme Low Bitrate Image Compression System for Mobile Depl...

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26th International Workshop on Multimedia signal processing

作者： Wu, Junqi Duan, Wenhong Ma, Xianping Chang, Jianhui Wang, Shanshe Ma, Siwei Jia, Chuanmin Peking Univ Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China

ISBN: (纸本)9798350387261;9798350387254

End-to-end image compression has achieved satisfactory results in recent studies. However, existing methods suffer from high complexity of complicated neural network computation and cannot be directly deployed on mobile devices due to the limitations of computing ability and storage. Therefore, considering the resource and computing ability constrains of the mobile devices, we make a trade-off in this paper between rate-distortion (R-D) performance, inference time, and model complexity. Then we design a novel lightweight perceptual image compression framework to alleviate the storage and complexity burden of mobile devices. Moreover, we design a hardware-friendly deployment scheme to apply the proposed compression framework on high-end mobile devices, which can achieve efficient image compression. Based on the above structures, we propose the first mobile system that achieves image compression on mobile devices. The supplementary material of our system demo is on https://***/documents/extreme-lowbitrate-image-compression-system-mobile-deployment.

关键词： image compression low bitrate mobile devices hardware-friendly

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Disentanglement of content and style features in multi-center cytology images via contrastive self-supervised learning

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BIOMEDICAL signal processing AND CONTROL 2024年 95卷

作者： Tian, Chongzhe Liu, Xiuli Cheng, Shenghua Bai, Jiaxin Chen, Li Zeng, Shaoqun Huazhong Univ Sci & Technol Britton Chance Ctr Wuhan Natl Lab Optoelect Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Biomed Photon Wuhan Natl Lab Optoelect MoE Wuhan Peoples R China Southern Med Univ Sch Biomed Engn Guangzhou Peoples R China Southern Med Univ Guangdong Prov Key Lab Med Image Proc Guangzhou Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Dept Clin Lab Wuhan Peoples R China

Multi -center cervical cytology images have various image styles due to the differences in staining and imaging techniques, which pose a significant challenge to the performance of automated cervical cancer diagnosis tools. We propose a dual -head network architecture that explicitly disentangles image features into content and style features, and applies contrastive self -supervised learning to a large number of unlabeled images, achieving enhanced generalization across various styles. We pretrain our model on 1,024,855 images cropped from 3,561 whole slide images (WSIs), and visualize the features using t -distributed stochastic neighbor embedding (t-SNE) method, demonstrating the effectiveness of our method in distinguishing between content and style features. In the downstream task, we evaluate our model on 192,123 binary -classified images with 10 styles, and achieve the best accuracy among all methods for every style. Across the 10 different data sources, our method attained an average accuracy of 80.4%, outperforming all other comparative methods by 3% to 17%, demonstrating our method's potential to enhance the performance and robustness of automated cytology image analysis in multi -center settings.

关键词： Multi-center cytology images Content and style feature disentanglement Contrastive self-supervised learning Cervical cancer Automated diagnostic tools

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Cascade dual-domain swin-conv-unet for MRI reconstruction

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BIOMEDICAL signal processing AND CONTROL 2024年 96卷

作者： Sheng, Jinhua Yang, Xiaofan Zhang, Qiao Huang, Pu Huang, He Zhang, Qian Zhu, Haodi Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Zhejiang Peoples R China Minist Ind & Informat Technol PR China Key Lab Intelligent Image Anal Sensory & Cognit Hl Hangzhou 310018 Zhejiang Peoples R China Beijing Hosp Beijing 100730 Peoples R China Chinese Acad Med Sci Inst Geriatr Med Beijing 100730 Peoples R China

Reconstructing magnetic resonance (MR) images from undersampled k-space data have always been a challenging problem. Compressed Sensing (CS) can reconstruct images from a small amount of sampled data when combined with the robust feature learning ability of deep learning, and it can further reduce the sampling time. Most previous deep learning methods using compressed sensing relied heavily on convolutional neural networks (CNNs) or swin transformer block(STB), even they reconstructed images through stacking or cross-domain structure. However, due to the limited size of their receptive fields, convolutional neural networks cannot explore the global features of images. Conversely, vast receptive fields would increase model complexity and make the entire network difficult to train. In this paper, we proposed a cascade dual-domain swin-conv unet for reconstruction(CDSCU-Net), which combines STB and CNNs to focus on both local and global features during reconstruction. By fusing these features through incorporating our designed residual modules in the skip connections, we can mine more refined feature representations. Compared with the best-performing deep learning reconstruction methods based on compressed sensing in recent years, CDSCU-Net can better preserve the structural details of images and achieve good reconstruction quality at lower acceleration factors, additionally, the reconstructed images can also serve as raw data for other tasks.

关键词： Swin-transformer Convolutional neural networks Compressed sensing Magnetic Resonance Imaging (MRI) Pixel shuffle Feature fusion

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Noise Identification for Data-augmented Physics-based State-Space Models 37

Noise Identification for Data-augmented Physics-based State-...

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2024 International Workshop on signal processing Systems

作者： Dunik, J. Straka, O. Kost, O. Tang, S. Imbiriba, T. Closas, P. Univ West Bohemia Dept Cybernet Univ 8 Plzen 30614 Czech Republic Northeastern Univ Dept Elect & Comp Engn 360 Huntington Ave Boston MA 02115 USA

ISBN: (纸本)9798350373769;9798350373752

This paper deals with the state-space modelling of nonlinear stochastic dynamic systems. The emphasis is laid on the emerging area of data-augmented physics-based modelling of the state dynamics, which combines the benefits of the physics-driven and data-based identified models. As the augmented state-space models depend on the measured data, modelling the state noise properties becomes challenging. This paper proposes and validates a concept for the state noise identification of nonlinear data-augmented state equation using the maximum likelihood and correlation-based methods. The numerical simulation of a tracking scenario shows significant improvement of the state estimation accuracy and consistency when using the identified noise model.

关键词： State estimation neural networks Correlation method Maximum likelihood method.

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Learning content-aware feature fusion for guided depth map super-resolution

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signal processing-image COMMUNICATION 2024年 126卷

作者： Zuo, Yifan Wang, Hao Xu, Yaping Huang, Huimin Huang, Xiaoshui Xia, Xue Fang, Yuming Jiangxi Univ Finance & Econ 665 Yuping West St Nanchang 330013 Jiangxi Peoples R China Shanghai Artificial Intelligence Lab Yunjing Rd 701 Shanghai 200232 Peoples R China

RGB-D data including paired RGB color images and depth maps is widely used in downstream computer vision tasks. However, compared with the acquisition of high -resolution color images, the depth maps captured by consumer-level sensors are always in low resolution. Within decades of research, the most state -of -the -art (SOTA) methods of depth map super -resolution cannot adaptively tune the guidance fusion for all feature positions by channel-wise feature concatenation with spatially sharing convolutional kernels. This paper proposes JTFNet to resolve this issue, which simulates the traditional Joint Trilateral Filter (JTF). Specifically, a novel JTF block is introduced to adaptively tune the fusion pattern between the color features and the depth features for all feature positions. Moreover, based on the variant of JTF block whose target features and guidance features are in the cross-scale shape, the fusion for depth features is performed in a bi-directional way. Therefore, the error accumulation along scales can be effectively mitigated by iteratively HR feature guidance. Compared with the SOTA methods, the sufficient experiment is conducted on the mainstream synthetic datasets and real datasets, i.e., Middlebury, NYU and ToF-Mark, which shows remarkable improvement of our JTFNet.

关键词： Convolutional neural network Joint trilateral filter Guided depth map super-resolution Content-dependent network

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Pseudo-Retinex decomposition-based unsupervised underwater image enhancement and beyond

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DIGITAL signal processing 2023年 137卷

作者： Shen, Zhen Xu, Haiyong Jiang, Gangyi Yu, Mei Du, Baozhen Luo, Ting Zhu, Zhongjie Ningbo Univ Sch Math & Stat Ningbo 315211 Peoples R China Ningbo Univ Fac Informat Sci & Engn Ningbo 315211 Peoples R China Zhejiang Wanli Univ Coll Informat & Intelligence Engn Ningbo 315211 Peoples R China

Underwater images suffer from color casts and low contrast degraded due to wavelength-dependent light scatter and abortion of the underwater environment. To effectively improve the quality of the underwater images, deep learning-based underwater image enhancement methods have been widely proposed. However, most deep learning-based underwater image enhancement methods rely heavily on paired datasets. Actually, obtaining distortion-free images as reference images is difficult in underwater imaging. To address this problem, a fully Unsupervised convolution neural network-based Underwater image Enhancement (UUIE) is proposed by pseudo-Retinex decomposition. The innovation of the proposed UUIE is to establish a relationship between the underwater imaging model and the Retinex model, then use terrestrial images to replace underwater images for training and estimate pseudo-illumination and pseudo-reflection maps through self-supervision using the pseudo-Retinex decomposition. The pseudo -reflection image and pseudo-illumination image are reconstructed by the pseudo-Retinex decomposition to obtain the enhanced image. Additionally, the proposed UUIE can also be extended to image dehazing and low-light enhancement with only one trained model. Experimental results on synthetic and real -world datasets demonstrate the effectiveness of the proposed UUIE quantitatively and qualitatively.(c) 2023 Elsevier Inc. All rights reserved.

关键词： Unsupervised underwater image enhancement Pseudo-Retinex decomposition Convolution neural network Low -light image enhancement image dehazing Retinex model

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