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International Conference on Computer and Communication Engineering, ICCCE

作者： Shivani Saxena Surbhi Mistry Nilesh Patel Mohendra Roy Ahsan Z. Rizvi Computer Sciences and Engineering Institute of Advanced Research Gandhinagar India Biotechnology and Bioengineering Institute of Advanced Research Gandhinagar India Information and Communication Technology Pandit Deendayal Energy University Gandhinagar India

Signa1 denoising is a crucial task in image processing, audio analysis, and communications. Traditional methods often struggle to strike a balance between reducing noise and preserving important signal details. Existing methods exploit the potential of fractional transforms combined with sparse representation techniques. However, these approaches face challenges in achieving the desired balance due to limited sensitivity to signal variations, high computational complexity, restricted adaptability to diverse noise types, and the need for careful selection of transform parameters. To overcome these limitations, we propose a novel method called Fractional Transform Sparse Representation Denoising (FTSRD), which integrates fractional wavelet transform (FWT), fractional DCT (FDCT), sparse representation, and Deep Denoising Autoencoder (DDAE) techniques. The integration of DDAE in FTSRD provides several advantages. DDAE, being a deep learning-based model, has the capability to learn complex noise patterns and extract meaningful signal features. Experimental evaluations were conducted on synthetic signals and ECG signals. The results demonstrate the superior performance of FTSRD in reducing noise while preserving essential structural details. It also surpasses traditional methods in subjective and objective quality measures. In conclusion, FTSRD addresses the challenges associated with noise reduction and signal detail preservation, making it a valuable tool in various denoising applications.

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2nd International Conference on VLSI, Communication and signal processing, VCAS 2019

2nd International Conference on VLSI, Communication and Sign...

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2nd International Conference on VLSI, Communication and signal processing, VCAS 2019

ISBN: (纸本)9789811568398

The proceedings contain 60 papers. The special focus in this conference is on VLSI, Communication and signal processing. The topics include: Rain Streaks Elimination Using Hybrid Median Filter and Contrast Stretching;design of Efficient Ternary Subtractor;A Multichannel Link-Layer Cooperation Protocol (MLCP) for Cognitive Radio Ad Hoc Network;Far Field Analysis of Defected Ground Structured Wideband Antenna for RF Energy Harvesting applications;New LMI Criterion to the Robust Stability of Discrete-Time Systems with Time-Varying Delays and Generalized Overflow Nonlinearities;PAPR Reduction in OFDM for VLC System;performance Evaluation of Energy-Efficient Adiabatic Logic Circuit-Based Multiplexer for Low Power applications;fault Detection and Classification in Microgrid Using wavelet Transform and Artificial Neural Network;design and Performance Evaluation of Highly Efficient Adders in Nanometer Technology;α- η- μ/ IG Composite Fading Model for Body-Centric Communication;DTMOS-Based Low-Voltage Low-Power CCII+ and Biquad Filter Using ±0.25 V Supply;structural and Optical Analysis of Bulk-Hetero Interface Between MoS2: Pentacene;An Efficient Watermarking Process Based on Three-Level DWT and FFT Technique;blockchain-Enabled Traceable, Transparent Transportation System for Blood Bank;A Compendious Analysis of Advances in HE Methods for Contrast Enhancement;a Survey of Semantic Segmentation on Biomedical images Using Deep Learning;HT-IWT-DCT-Based Hybrid Technique of Robust image Watermarking;Reliability and Circuit Timing Analysis with HCI and NBTI;Performance of 4H-SiC IMPATT Diode at Ka- and W-Band with Temperature Variation;realization of a Low Profile, Wideband Omni-directional Antenna for Ku-band Airborne applications;effect of Structural Metal on Metamaterial-Based Absorber Performance;development of Cloud-Based Multi-Modal m-Cardiac Management System.

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Research on high-resolution quantum medical image technology for COVID-19 28

Research on high-resolution quantum medical image technology...

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Conference on Infrared Remote Sensing and Instrumentation XXviii held at SPIE Optical Engineering + applications Conference

作者： Bi, Siwen Chen, Hao Liu, Jiaying Shenzhen Jingwan Quantum Remote Sensing Sci & Te Shenzhen 518108 Guangdong Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100101 Peoples R China

ISBN: (纸本)9781510638112;9781510638105

Considering the disadvantages of low definition, low image contrast and brightness, inconspicuous details, which make image analysis more complicate, we developed quantum medical image processing system software. It includes quantum image enhancement module, quantum image denoising module and quantum image segmentation module, which respectively study the quantum enhancement algorithm, quantum denoising algorithm and quantum segmentation algorithm. Quantum denoising algorithm firstly add noise to the image and then carries out a logarithmic transfo nation and a double density dual-tree complex wavelet transfoun on the noise-added image, later a denoising of the wavelet coefficients based on Bayesian theory, and the Maxaposterior (MAP) is perfouned to estimate the variance of the double-tree complex wavelet. Finally the denoised image is obtained from the inverse transfoun of the dual-tree complex wavelet. Quantum enhancement algorithm improves image quality through quantum inspired and unsharp masking. First, a quantum enhancement operator based on quantum superposition state theory was constructed to enhance image contrast ratio, then the processed image quality was improved by unsharp masking. Quantum segmentation algorithm searches automatically the optimal threshold through improved quantum genetic algorithm, which reduces the complexity of program and improves the infounation entropy compared with traditional methods. More image infounation can be retained and more ideal segmentation effect is realized through the algorithm. Experiment show that the peak signal-to-noise ratio (PSNR) for the quantum algorithm is improved by over 2dB and the edge retention index (EPI) is 0.1 higher than that for common methods, image info nation entropy and clarity index are significantly improved.

关键词： Quantum algorithm medical image processing peak signal-to-noise ratio (PSNR) edge-preserving index (EPI) information entropy

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Scalable Mesh Representation for Depth from Breakpoint-Adaptive wavelet Coding 22

Scalable Mesh Representation for Depth from Breakpoint-Adapt...

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22nd IEEE International Workshop on Multimedia signal processing (MMSP)

作者： Li, Yue Mathew, Reji Taubman, David Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia

ISBN: (纸本)9781728193205

A highly scalable and compact representation of depth data is required in many applications, and it is especially critical for plenoptic multiview image compression frameworks that use depth information for novel view synthesis and interview prediction. Efficiently coding depth data can be difficult as it contains sharp discontinuities. Breakpoint-adaptive discrete wavelet transforms (BPA-DWT) currently being standardized as part of JPEG 2000 Part-17 extensions have been found suitable for coding spatial media with hard discontinuities. In this paper, we explore a modification to the original BPA-DWT by replacing the traditional constant extrapolation strategy with the newly proposed affine extrapolation for reconstructing depth data in the vicinity of discontinuities. We also present a depth reconstruction scheme that can directly decode the BPA-DWT coefficients and breakpoints onto a compact and scalable mesh-based representation which has many potential benefits over the sample-based description. For performing depth compensated view prediction, our proposed triangular mesh representation of the depth data is a natural fit for modern graphics architectures.

关键词： depth coding scalable compression mesh representation adaptive wavelet transforms coding standards

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Radar signal Modulation Recognition Based on Sep-ResNet

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SENSORS 2021年第22期21卷 7474-7474页

作者： Mao, Yongjiang Ren, Wenjuan Yang, Zhanpeng Chinese Acad Sci Inst Elect Key Lab Network Informat Syst Technol Beijing 100190 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time-frequency (T-F) analysis and a deep neural network to identify radar modulation signals. The T-F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T-F images. Adaptive filtering and morphological processing are used in T-F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T-F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is -10 dB, the probability of successful recognition (PSR) is 93.44%.

关键词： radar modulation signal time-frequency analysis complex Morlet wavelet image enhancement channel-separable ResNet

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Performance analysis of DWT families 3

Performance analysis of DWT families

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3rd International Conference on Intelligent Sustainable Systems, ICISS 2020

作者： Rashid, Owais Amin, Asdaq Lone, Mohd Rafi School of Engineering and Technology Baba Ghulam Shah Badshah University Department of Electronics and Communication Engineering Rajouri JandK185234 India

ISBN: (纸本)9781728170893

Discrete wavelet transform (DWT) is a technique which is used to acquire wavelets from image pixels. It transforms a discrete time signal to discrete wavelet representation. The DWT has shown great performance in digital image compression, demodulation, demultiplexing and denoising applications. In this paper, a comparison among different wavelet transforms is made. These wavelets include;Discrete Meyer, Biorthogonal, Fejer-korovkin, Daubechies, Coiflets, Symlets and Reverse Biorthogonal wavelets. Each wavelet is implemented for upto six levels of wavelet decomposition. The reconstructed images are then compared with the original images and their performance is evaluated with the help of Peak signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE). It has been observed in all wavelets that as we increase the decomposition level, MSE increases and PSNR decreases. In this paper, a comparison has been made between the different wavelet families, keeping the filter length fairly same in all the wavelets. This paper will help the new researchers of this field to choose the suitable wavelet transform for image processing applications. © 2020 IEEE.

关键词： Discrete wavelet transforms

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image deconvolution and PSF reconstruction with STARRED: a wavelet-based two-channel method optimized for light-curve extraction

arXiv

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arXiv 2024年

作者： Millon, Martin Michalewicz, Kevin Dux, Frédéric Courbin, Frédéric Marshall, Philip J. Kavli Institute for Particle Astrophysics and Cosmology Department of Physics Stanford University StanfordCA94305 United States Department of Mathematics Imperial College London LondonSW7 2AZ United Kingdom European Southern Observatory Alonso de Córdova 3107 Vitacura Santiago Chile Observatoire de Sauverny Versoix1290 Switzerland SLAC National Accelerator Laboratory Menlo ParkCA94025 United States

We present starred, a Point Spread Function (PSF) reconstruction, two-channel deconvolution, and light curve extraction method designed for high-precision photometric measurements in imaging time series. An improved resolution of the data is targeted rather than an infinite one, thereby minimizing deconvolution artifacts. In addition, starred performs a joint deconvolution of all available data, accounting for epoch-to-epoch variations of the PSF and decomposing the resulting deconvolved image into a point source and an extended source channel. The output is a high signal-to-noise, high resolution frame combining all data, and the photometry of all point sources in the field of view as a function of time. Of note, starred also provides exquisite PSF models for each data frame. We showcase three applications of starred in the context of the imminent LSST survey and of JWST imaging: i) the extraction of supernovae light curves and the scene representation of their host galaxy, ii) the extraction of lensed quasar light curves for time-delay cosmography, and iii) the measurement of the spectral energy distribution of globular clusters in the "Sparkler", a galaxy at redshift z=1.378 strongly lensed by the galaxy cluster SMACS J0723.3-7327. starred is implemented in jax, leveraging automatic differentiation and GPU acceleration. This enables rapid processing of large time-domain datasets, positioning the method as a powerful tool for extracting light curves from the multitude of lensed or unlensed variable and transient objects in the Rubin-LSST data, even when blended with intervening objects. Copyright © 2024, The Authors. All rights reserved.

关键词： Supernovae

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Deep learning-based classification of multichannel bio-signals using directedness transfer learning

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BIOMEDICAL signal processing AND CONTROL 2022年第PartA期72卷 103300-103300页

作者： Bahador, Nooshin Kortelainen, Jukka Univ Oulu Ctr Machine Vis & Signal Anal MRC Oulu Physiol Signal Anal Team Oulu Finland

The problem with processing of multivariate/multichannel signals lies in adapting of existing classifiers on data. Reformulating time-series data as visual clues and assigning visual patterns to different categories help the classification of time series in a wide range of applications. These series-to-image transformations have benefits including better noise robustness and more options regarding augmentation. They also provide the possibility of achieving discriminative features by employing transfer learning paradigm in cases dealing with highly small training datasets. In this respect, this work aimed to encode spectral-phase information into a bi-dimensional map. Transferring knowledge was done using bi-dimensional transformation capitalizing on the direction and propagation pattern of one channel influence on the others. EEG data from patients diagnosed with delirium (N = 15) recorded using a 10-channel BrainStatus device were used for this analysis. Considering leave-one-subject-out cross-validation, classification outcomes demonstrated that directedness transfer learning via Alexnet yields a promising performance showing 97.17% precision and outperforming other approaches. Comparison with nine different deep networks pretrained on imageNet database was included. Directedness transfer learning resulted in precision of 95.29 +/- 1.46 (mu +/- sigma)% among all networks. For further evaluation, directedness bi-dimensional transformation was also compared with six other 2D maps. Applying different networks resulted in average precision of (91.99 +/- 2.23)% for polar-, (91.69 +/- 1.57)% for correlation-, (90.46 +/- 1.71)% for Spectrogram-, (87.82 +/- 2.16)% for wavelet-, (84.24 +/- 1.72)% for Wigner-Ville-and (82.84 +/- 2.46)% for Mel-frequency Cepstrum maps. To conclude, the proposed technique shows significant benefit in compressing spatio-spectral patterns of multichannel signals in just a unified visual representation.

关键词： Spectral-phase-spatial fusion Multivariate data Transfer learning Deep learning Bi-dimensional mapping

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Rationality Modeling of 3D Scanning image of Digital Print Laser Based on Block Matching Texture Information Repair

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IEEE SENSORS JOURNAL 2020年第20期20卷 11975-11982页

作者： Zheng, Xiaoqiang Luoyang Normal Univ Acad Art & Design Luoyang 471934 Peoples R China

The accuracy of digital engraving image modeling of traditional modeling methods cannot be effectively guaranteed. To this end, an image solid modeling method based on laser 3D scanning is proposed. First, the pixel to be repaired is determined according to the priority value of the damaged area, and the neighborhood of the pixel is subjected to block matching processing. Then, using the structural similarity between the block to be repaired and each matching block, a corresponding redundant dictionary is established;the damaged region is repaired by solving the sparse representation problem. Constructing an information conduction model of the digital scanning laser three-dimensional scanning image, collecting the information of the digital scanning laser three-dimensional scanning image, and analyzing its characteristics;then performing priority determination of unknown pixels of the digital printing;then using the 5-level wavelet decomposition method for laser three-dimensional scanning image feature decomposition and information fusion to achieve rationality modeling of digital print laser 3D scanning images. The simulation results show that the laser scanning modeling method is fast and accurate, and can control the modeling error to a low level. The method of reconstructing the three-dimensional scanned image of digital engraving laser has better repair performance, higher feature fusion degree, higher peak signal-to-noise ratio, improved image quality, and more in print restoration and digital reconstruction applications

关键词： Laser modes Three-dimensional displays Solid modeling Surface emitting lasers Maintenance engineering Laser beams Laser fusion Laser three-dimensional scanned image print repair block matching

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Theoretical Analysis of Quaternion image Matching Performance

Theoretical Analysis of Quaternion Image Matching Performanc...

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Conference on Mobile Multimedia/image processing, Security, and applications

作者： DelMarco, Stephen P. BAE Syst 600 Dist Ave Burlington MA 01803 USA

ISBN: (纸本)9781510635760;9781510635753

Recently, quaternions have been applied to multi-channel image processing such as color image registration, edge detection, and image matching. The general approach consists of using up to four image components as separate components in a quaternion-valued image. Techniques from quaternion algebra and analysis, such as quaternion Fourier transforms and correlation, are exploited to implement the desired image processing operations. The motivation underlying use of quaternions involves the concept of holistic data processing. That is, information in separate channels is processed jointly, thereby leveraging correlations across channel. Furthermore, quaternion transforms, such as the Fourier and wavelet transforms, have been defined and can be applied to process quaternion-valued imagery. For example, analogues from classical signal processing, such as the convolution theorem, have been derived and can be used to reduce computational burden. Such quaternion-based image processing techniques can provide performance superior to classical approaches which process image components independently. However, under certain conditions, performance of quaternion-based methods fails to meet performance of independent channel processing methods. In this paper, we perform a theoretical analysis of the performance of quaternion image matching. We present conditions under which quaternion-based processing outperforms, or falls short of, independent channel processing methods. We produce numerical image matching results validating the theoretical performance analysis using synthetic data. We define and solve a linear system of equations to generate the synthetic data. Receiver Operating Characteristics curves are provided to demonstrate the improved performance of the quaternion image matching approach under the favorable conditions predicted by the theory.

关键词： hypercomplex quaternions correlation multi-channel image matching

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