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Single-pixel compressive imaging in shift-invariant spaces via exact wavelet frames

signal processing-image COMMUNICATION 2022年第0期105卷 1页

作者： Vlasic, Tin Sersic, Damir Univ Zagreb Fac Elect Engn & Comp Dept Elect Syst & Informat Proc Unska 3 HR-10000 Zagreb Croatia

This paper introduces a novel framework for single-pixel imaging via compressive sensing (CS) in shift-invariant (SI) spaces by exploiting the sparsity property of a wavelet representation. We reinterpret the acquisition procedure of a single-pixel camera as filtering of the observed signal with continuous-domain functions that lie in an SI subspace spanned by the integer shifts of the box function. The signal is modeled by an arbitrary SI generator whose special case is the box function, which, as we show in the paper, is conventionally used in single-pixel imaging. We propose to use separable B-spline generators which are intuitively complemented by sparsity-inducing spline wavelets. The SI models of the acquisition and the underlying signal lead to an exact discretization of an inherently continuous-domain inverse problem to a finite-dimensional problem of CS type. By solving the CS optimization problem, a parametric representation of the signal is obtained. Such a representation offers many practical advantages in image processing applications. We propose an efficient matrix-free implementation of the framework and conduct it on the standard test images and real-world measurement data. Experimental results show that the proposed framework achieves a significant improvement of the reconstruction quality relative to the conventional discretization in CS setups.

关键词： Compressed sensing Discrete wavelet transform image sampling Inverse problems Splines

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An Efficient Discrete wavelet Transform Architecture with Low Power and Multiplier-Less Structure for Pervasive Biomedical image processing Application

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EAI Endorsed Transactions on Pervasive Health and Technology 2023年第1期9卷

作者： Guptha, Maram Anantha Rao, Surampudi Srinivasa Selvaraj, Ravindrakumar ECE Department Sri Satya Sai University of Technology and Medical Science Madhya Pradesh India Malla Reddy College of Engineering and Technology Telangana 500014 India Biomedical Engineering Sri Shakthi Institute of Engineering and Technology Tamilnadu Coimbatore641062 India

INTRODUCTION: Over the past several years analysis of image has moved from larger system to pervasive portable devices. For example, in pervasive biomedical systems like PACS-Picture achieving and Communication system, computing is the main element. image processing application for biomedical diagnosis needs efficient and fast algorithms and architecture for their functionality. Future pervasive systems designed for biomedical application should provide computational efficiency and portability. The discrete wavelet transform (DWT) designed in on-chip been used in several applications like data, audio signal processing and machine learning. OBJECTIVES: The conventional convolution based scheme is easy to implement but occupies more memory, power and delay. The conventional lifting based architecture has multiplier blocks which increase the critical delay. Designing the wavelet transform without multiplier is a effective task especially for the 2-D image analysis. Without multiplier Daubechies wavelet implementation in forward and inverse transforms may find efficient. The objective of the work is on obtaining low power and less delay architecture. METHODS: The proposed lifting scheme for two dimensional architecture reduces critical path through multiplier less and provides low power, area and high throughput. The proposed multiplier is delay efficient. RESULTS: The architecture is Multiplier less in the predict and update stage and the implementation carried out in FPGA by the use of Quartus II 9.1 and it is found that there is reduction in consumption of power at approximately 56%. There is reduction in delay due to multiplier less architecture. CONCLUSION: multiplier less architecture provides less delay and low power. The power observed is in milliwatts and suitable for high speed application due to low critical path delay. © 2023 Maram Anantha Guptha et al.

关键词： Discrete wavelet transforms

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A Decentralized GPS Positioning Consensus Based on Secure image Watermarking for UAV Networks

A Decentralized GPS Positioning Consensus Based on Secure Im...

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Innovative and Intelligent Information Technologies (IC3IT), International Conference on

作者： Brahim Ferik Lakhdar Laimeche Abdallah Meraoumia Abdelkader Laouid Ahcene Bounceur Mostefa Kara Laboratory of Mathematics Informatics and Systems (LAMIS) University of Larbi Tebessi Tebessa Algeria Artificial Intelligence and its Applications Laboratory (LIAP) University of El Oued El Oued Algeria Information Systems Department University of Sharjah Sharjah United Arab Emirates Information & Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (数字)9798331541064

ISBN: (纸本)9798331541071

The popularity of Unmanned Aerial Vehicles (UAVs) in recent years has accelerated the demand for robust image security to secure communication and authenticate images or videos from usage by unauthorized parties who may seek access to sensitive information. Having unauthorized and deceptive communication, from interference in signals to data manipulation, will be a challenge. This paper presents a novel approach to integrating GPS positioning with secure image capture and decentralized coordination for UAVs. Using EI-Gamal encryption and Magic Number Fragmentation, GPS coordinates are securely encrypted, and GPS data into captured images through a hybrid Discrete wavelet Transform-Discrete Cosine Transform (DWT-DCT) watermarking technique. A consensus mechanism ensures decentralized and privacy-preserving computation of secure meeting points for UAV s. Moreover, the system maintains high image quality, with superior PSNR and SSIM values, while demonstrating resilience against signal processing and geometric attacks. These findings affirm the method's ability to extract encrypted GPS coordinates reliably, enhancing UAV network security.

关键词： Watermarking Transforms signal processing Autonomous aerial vehicles Discrete wavelet transforms Encryption Consensus protocol Discrete cosine transforms Global Positioning System Videos

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Optimizing Compressed Sensing with Taylor Sunflower Algorithm: A Performance Analysis of Hybrid Techniques for Biomedical Imaging and Video Surveillance

Optimizing Compressed Sensing with Taylor Sunflower Algorith...

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Innovations in High Speed Communication and signal processing (IHCSP), International Conference on

作者： Asha Rajiv Santosh Kumar S. Arul Antran Vijay Prabhat Sharma Ranjusha J P Prateek Aggarwal School of Sciences Physics & Electronics JAIN (Deemed to be University) Bangalore Karnataka India AIDS Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam College of Engineering Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

A powerful area that seeks to minimize the amount of space of signals that are compressible or sparse in a specific base representation that uses compressed recognizing in video streaming applications. A sparse signal provides a good approximation of the signal since the representation is sparse and the amplitude of the coefficient’s decays according to the power law. The process of projecting signals into a space with low dimensions results in measurement vectors. A sparse signal may be accurately reconstructed using the measurement vector. Imprecise or noisy data that may nevertheless be compressed and used for approximation purposes. images in the frequency or wavelet domain are suitable for Compressed Sensing because they are inherently compressible. Reconstructed images are of higher quality, and the compression ratio—the ratio of an image's pixel count to its measurement vector—has been reduced, enabling more precise reconstruction. Taylor SFO based CS is a two-stage optimization technique for picture compression and recovery that aims to enhance the quality of models with high correlation and low redundancy. An image that uses self-replication and 3D change to compress itself is first processed in the early phase. In the last step, the CS uses SSIM to recover pictures using an ensemble sparseness estimate and Taylor SFO parameters.

关键词： image coding wavelet domain Three-dimensional displays Frequency-domain analysis Biomedical measurement Streaming media Extraterrestrial measurements Vectors Noise measurement image reconstruction

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Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT image Analysis and an Optimized DBN-GA-LSSVM Framework

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SENSORS 2024年第12期24卷 4009页

作者： Siddique, Muhammad Farooq Ahmad, Zahoor Ullah, Niamat Ullah, Saif Kim, Jong-Myon Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea PD Technol Cooperat Ulsan 44610 South Korea

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications.

关键词： continuous wavelet transforms deep belief network genetic algorithm least squares support vector machine

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Dual-Interference Channels Static Fourier Transform Imaging Spectrometer Based on Stepped Micro-Mirror: Data processing and Experiment Research

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REMOTE SENSING 2024年第2期16卷 407页

作者： Liu, Guohao Liang, Jingqiu Lv, Jinguang Zhao, Baixuan Zhao, Yingze Zheng, Kaifeng Chen, Yupeng Qin, Yuxin Wang, Weibiao Wang, Shurong Sheng, Kaiyang Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys State Key Lab Appl Opt Changchun 130033 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Key Lab Opt Syst Adv Mfg Technol Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Foshan Univ Sch Phys & Optoelect Engn Guangdong Hong Kong Macao Joint Lab Intelligent Mi Foshan 528225 Peoples R China

The use of a dual-interference channels static Fourier transform imaging spectrometer based on stepped micro-mirror (D-SIFTS) for environmental gas monitoring has the advantages of high throughput, a compact structure, and a stable performance. It also has the characteristics of both a broad spectral range and high spectral resolution. However, its unique structural features also bring many problems for subsequent data processing, mainly including the complex distribution of the interference data, the low signal-to-noise ratio (SNR) of infrared scene images, and a unique inversion process of material information. To this end, this paper proposes a method of image and spectra information processing and gas concentration inversion. A multiscale enhancement algorithm for infrared images incorporating wavelet denoising is used to obtain high-quality remote sensing scene images, and spectral reconstruction optimization algorithms, such as interference intensity sequence resampling, are used to obtain accurate spectral information;the quantitative calibration model of the detected gas concentration is established to achieve high-precision inversion of gas concentration, and its distribution is visualized in combination with the scene image. Finally, the effectiveness and accuracy of the data processing algorithm are verified through the use of several experiments, which provide essential theoretical guidance and technical support for the practical applications of D-SIFTS.

关键词： Fourier transform imaging spectrometer stepped micro-mirror image enhancement and denoising spectrum reconstruction gas inversion and concentration distribution visualization

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WaveDiff: Underwater Visual Data Enhancement with wavelet-Accelerated Diffusion

WaveDiff: Underwater Visual Data Enhancement with Wavelet-Ac...

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OCEANS

作者： Shrutika Vishal Thengane Yu Xiang Tan Marcel Bartholomeus Prasetyo Malika Meghjani Singapore University of Technology and Desin Singapore

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

Unmanned underwater vehicles generally rely on high quality visual data and low latency for the applications of monitoring, exploration and search. Several methods have been proposed to improve underwater visibility by enhancing images taken by cameras using generative methods such as diffusion models. Although diffusion models have proven very useful for visual data enhancement, they enhance images iteratively, making this process computationally inefficient. Current methods primarily focus on improving visibility without addressing operation latency or speed, rendering these methods unsuitable for low bandwidth or fast information tracking. To overcome this, we propose using discrete wavelet transform (DWT) to decompose the image into four frequency components, reducing the spatial dimension. Out of these four components, we use only one as part of the diffusion process. At the end of the diffusion process, we apply the inverse discrete wavelet transform to obtain the enhanced image. This simple strategy helps reduce processing time by 50% with minimal loss in Peak signal to Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) on the LSUI dataset.

关键词： Training Visualization PSNR Diffusion processes Sea measurements Transforms Diffusion models Loss measurement Time measurement Discrete wavelet transforms

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Multiscale wavelet-based regularized reconstruction algorithm for three-dimensional compressed sensing magnetic resonance imaging

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signal image AND VIDEO processing 2021年第7期15卷 1487-1495页

作者： Islam, Md Shafiqul Islam, Rafiqul Dhaka Univ Engn & Technol Dept Comp Sci & Engn Gazipur Bangladesh

Nowadays, the most dynamic and safe imaging technique used in hospitals to diagnose is magnetic resonance imaging (MRI). In clinical applications, such as follow-up of patients with planning surgery, radiation therapy, therapy response in bone metastases assessment and treatment for brain disorders, repeated MRI scans are performed. Unfortunately, the slow acquisitions of MRI drive cost high by limiting patient throughput as well as limiting the potential indications for use. A mathematical framework called compressed sensing (CS) to accelerate MRI acquisition is used for generating undersampled measurements. Then, the iterative nonlinear numerical method is required for perfect reconstruction from highly undersampled measurements. In this work, the multiscale wavelet domain regularization prior-based iterative reconstruction algorithm is developed to address the above-mentioned reconstruction problem, in which multiscale wavelet domain generalized Gaussian mixture model is utilized for the regularization prior. The regularization-based algorithm is applied to generate three-dimensional (3D) high-quality MRI volume from multichannel k-space measurements in a compressed sensing framework. Several experiments using one synthetic and one real 3D CS multichannel k-space data were used to evaluate and validate the performance of the proposed algorithm. The total variation (TV)-based and soft-thresholding (ST) based-reconstruction methods were implemented to compare with the proposed algorithm. Extensive experimental results demonstrated that the proposed method has improved results in terms of visual quality and quantitative measurements compared to the existing methods.

关键词： Compressed sensing Magnetic resonance imaging image reconstruction Generalized Gaussian mixture model 3D medical imaging

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Enhanced Laboratory Safety Education Through Interactive applications of Machine Learning-Boosted image processing Technologies

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TRAITEMENT DU signal 2023年第6期40卷 2623-2633页

作者： Zhang, Lijiao Wu, Jianzhen Wei, Jinxin Yu, Xiaoyan Yu, Jie Yuan, Bo Fuzhou Univ Mech & Elect Engn Practice Ctr Fuzhou 350108 Peoples R China Fujian Vocat Coll Agr Fuzhou 350007 Peoples R China Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China Quanzhou Bingdian Technol Co LTD Quanzhou 362000 Peoples R China

Laboratory safety education is fundamental to the smooth conduct of scientific research. Traditional educational models are often limited in interactivity and real-time feedback and they struggle to satisfy the increasing needs of laboratory safety management. This study aims to enhance the interactivity and efficacy of laboratory safety education by adopting machine learning-enhanced image processing technologies. To cope with the noise issue in hazardous behavior data within laboratories, wavelet threshold denoising is applied to significantly improve data usability. To deal with the imbalance in data samples of laboratory hazard behaviors, an adaptive boundary data augmentation algorithm is introduced to balance the dataset and strengthen the model's generalization capability. A breakthrough in extracting spatio-temporal features of hazardous behaviors in laboratories is achieved through an improved Spatio-Temporal Graph Convolutional Network (STGCN) model, enabling effective recognition and classification of hazardous behaviors. The outcome attained in this study has important implications for enhancing the interactivity and practicality of laboratory safety education and it can expand new research directions for the application of machine learning in the field of image processing.

关键词： laboratory safety education machine learning, image processing wavelet threshold denoising data augmentation Spatio-Temporal Graph Convolutional Network (ST-GCN) hazardous behavior recognition

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Dictionary plus wavelet Model With Nested-Minorized VB-EM for SMS-CAIPI R-fMRI Reconstruction

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2021年 2卷 383-395页

作者： Kulkarni, Prachi H. Merchant, S. N. Awate, Suyash P. Indian Inst Technol Dept Elect Engn Mumbai 400076 Maharashtra India Indian Inst Technol Dept Comp Sci & Engn Mumbai 400076 Maharashtra India

Resting-state functional magnetic resonance imaging (R-fMRI) applications can entail a higher temporal-sampling rate that trades off spatial resolution, thereby challenging effective scientific studies. To enable higher spatial resolution, current schemes speedup per-timeframe scanning by reconstruction from simultaneous multislice (SMS) magnetic resonance imaging (MRI) with k-space undersampling (sometimes temporal undersampling), while using prior models on the signal. We propose a novel algorithmic framework to reconstruct R-fMRI (SMS with controlled aliasing) that has, both, k-space undersampling and temporal undersampling. We propose a coupled spatiotemporal sparse model, incorporating (i) a robust spatially-regularized temporal-dictionary prior and (ii) a spatiotemporal wavelet prior, which we fit efficiently using variational Bayesian expectation maximization with nested minorization (VBEMNM). We show that our framework has the potential to enable higher spatial resolution without increasing scan time in R-fMRI that has inherently weak signals and is therefore prone to large physiological fluctuations, acquisition noise, and imaging artifacts. Qualitative and quantitative evaluation on retrospectively undersampled brain R-fMRI shows that estimates of resting-state networks from our framework and the boost in temporal stability given by our framework compares favourably to existing methods for R-fMRI reconstruction.

关键词： image reconstruction Imaging Functional magnetic resonance imaging Dictionaries Spatiotemporal phenomena Task analysis Spatial resolution R-fMRI reconstruction SMS with CAIPI joint k-t undersampling coupled dictionary and wavelets robust variational Bayesian EM nested minorization

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