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13th international symposium on image and signal processing and analysis, ispa 2023

作者： Avagyan, Shushik Katkovnik, Vladimir Egiazarian, Karen Tampere University Finland

ISBN: (纸本)9798350315363

In this paper, the problem of generating (hallucinating) a high-resolution hyperspectral image from a single low-resolution raw-RGB image is considered. To solve this problem, a general learning-based framework is proposed. It consists of two modules: a data adaptation module, and a backbone, deep feature extraction module. the data adaptation module is a shallow network consisting of pixel shuffling/unshuffling and shallow feature extraction. the deep feature extraction module which is an inherent part of many spectral reconstruction networks, aims at spectral super-resolution. Different spectral reconstruction networks have been studied as the backbone modules in the proposed framework. As a result of extensive simulations, it has been demonstrated that the proposed solution significantly outperforms the sequential approach of combining several state-of-the-art methods of image demosaicing, denoising, spatial and spectral super-resolution (by up to 6 dB in PSNR), and has large savings in the computational complexity (by over 5 times) with respect to the sequential method. © 2023 IEEE.

关键词： Feature extraction

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Compressed Sensing via Collaboratively Learned Dictionaries 12

Compressed Sensing via Collaboratively Learned Dictionaries

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12th international symposium on image and signal processing and analysis (ispa)

作者： Guo, Kai Liang, Xijun Lu, Weizhi Shandong Univ Sch Control Sci & Engn Jinan Peoples R China China Univ Petr Coll Sci Qingdao Peoples R China

ISBN: (纸本)9781665426398

In compressed sensing, the recovery error of a high dimensional signal can be approximately modeled by a multivariate Gaussian distribution N (mu, sigma I-2). the mean vector mu has its zero and nonzero elements correspond respectively to small dense errors caused by system noise, and large sparse errors caused by discarding relatively small coefficients in sparse recovery. To suppress small errors with zero mean, one major solution is to average the recovery results of multiple dictionaries. this will linearly decrease the error's variance sigma(2) , and then enable the error taking zero value with high probability. Unfortunately, the averaging method cannot promise to decrease large errors with nonzero means. Moreover, in practice, large errors of distinct dictionaries tend to occur at the same coordinates with the same value signs, because the dictionaries learned independently tend to converge to the points close to each other and thus yield similar large errors in sparse recovery. this property prevents large errors from being decreased by average. In the paper, we prove that the average performance could be improved, if large errors of distinct dictionaries have disjoint supports. To obtain such dictionaries, we propose a collaborative dictionary learning model, which is implemented with a block coordinate decent method. the resulting dictionaries present desired experimental performance. A full version of the paper is accessible at https://***/file/d/1_wy455PuKit1yf6QmXJxt81Y-ZZ5gq0s/view?usp=sharing

关键词： Dictionaries Collaboration Machine learning signal processing Programming Gaussian distribution Complexity theory

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2022 6th international symposium on Instrumentation Systems, Circuits and Transducers, INSCIT 2022 - Proceedings

2022 6th International Symposium on Instrumentation Systems,...

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6th international symposium on Instrumentation Systems, Circuits and Transducers, INSCIT 2022

ISBN: (纸本)9781665471688

the proceedings contain 16 papers. the topics discussed include: application of wavelet transform for ultrasonic time of flight estimation;a study about solar tracker for the performance of photovoltaic plants;hybrid integrator topology with digital input control and analog signal processing;development of a baseband amplifier for a 130 nm CMOS process using open-source design flow;a novel approach to assessing crop health by electrical impedance spectroscopy;evaluation of a wireless power transfer system with Helmholtz and 3D coils for biomedical capsules;soft growing robot to enable monitoring applications in remote constrained environments;a performance analysis of reconstruction algorithms for an analog-to-information converter architecture;plant health evaluation based on bioimpedance phase measurement;new approach insulator failure differentiation based on image processing;and application of data fusion techniques and IoT tools in an ultrasonic level measurement system.

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Optimized analysis of Mechanical Vibration signals through Improved Wavelet threshold Denoising

Optimized Analysis of Mechanical Vibration Signals Through I...

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Robotics & Intelligent Manufacturing Technology (ISRIMT), international symposium on

作者： Daoqu Geng Husheng Tong School of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798331528942

ISBN: (纸本)9798331528959

Wavelet transform is highly regarded as a mathematical theory and method in the fields of signal analysis and image processing. Vibration signals are crucial in mechanical fault diagnosis, but the collected vibration signals usually contain noise, so the vibration signals need to be denoised first. In wavelet denoising, the hard threshold function easily triggers oscillations, while the soft threshold function has the problem of constant deviation. this paper proposes the enhanced threshold function for vibration signal denoising on the basis of previous research, and evaluates the denoising effect with the signal-to-noise ratio and the mean-square error as the indexes, and the results show that choosing the appropriate wavelet basis function and decomposition level, combining with the adaptive threshold rule and the enhanced threshold function, the signal-to-noise ratio can be effectively improved and the mean-square error can be reduced. this study provides a practical solution for signal processing in the field of mechanical fault diagnosis.

关键词： Vibrations Wavelet transforms Fault diagnosis thresholding (Imaging) Noise reduction Mean square error methods Wavelet analysis Feature extraction signal denoising signal to noise ratio

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Grad-CAM-Based Classification of Chest X-Ray images of Pneumonia Patients 1

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6th international symposium on Advances in signal processing and Intelligent Recognition Systems, SIRS 2020

作者： Seerala, Pranav Kumar Krishnan, Sridhar Department of Data Science and Analytics Ryerson University 350 Victoria St Toronto ONM5B 2K3 Canada

ISBN: (数字)9789811604256

ISBN: (纸本)9789811604249

Publicly available datasets of Chest X-ray images have been accessible and available over the last few years. Medical image segmentation and classification have been prominent areas of research, since the release of such datasets. In this work, an attempt has been made to come up with a neural network with a limited number of parameters, with a goal of classifying chest X-rays of pneumonia from healthy patients. the intended applications would be edge devices such as cellular phones, Raspberry Pi’s, and other computing devices that could be used in developing countries which might be lacking in hardware to deploy and update the model. the dataset used is primarily on pediatric patients, and this paper demonstrates the usage of image segmentation and training data selection, to train on images with the most meaningful information, rather than the entire dataset. the segmentation model coupled with image post-processing has demonstrated robustness in classifying chest X-rays of external datasets, which could be used as a standalone tool for other image analysis projects. the results of the hyper-parameter tuned classification model show a dramatic improvement in overall accuracy of the test set when compared to other Kaggle kernels that have worked on the same data. Class activation maps (Grad-CAM) from the final convolution layer depict the regions of the image that contributed most towards determining the class. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image classification

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Periocular Recognition Under Unconstrained image Capture Distances 1

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6th international symposium on Advances in signal processing and Intelligent Recognition Systems, SIRS 2020

作者： Ipe, Vineetha Mary thomas, Tony Indian Institute of Information Technology and Management Thiruvananthapuram Kerala India

ISBN: (数字)9789811604256

ISBN: (纸本)9789811604249

In recent times, the periocular region has transpired to be a promising biometric modality for unconstrained human authentication. the latest trends in biometric research focus on removing the distance constraint faced while capturing the biometric images. Easing such restraints results in further challenges in the form of resolution drop and quality degradation. Furthermore, the use of improved technologies like that of image cameras and lenses with high resolution and focal length is not always feasible and economical. Many image enhancement techniques such as interpolation methods and superresolution were developed to address the quality issue of acquired images arising due to the relaxation of range constraints. However, the use of interpolation techniques results in a blurred image. In addition, from our investigations, we have inferred that the conventional CNN based superresolution methods improve the resolution of images alike without taking the capture range into account and hence are not quality driven. In order to improve the recognition rate irrespective of the acquisition distance, we propose to make use of transfer learning. the novelty of our approach is that it is the premier research that analyses the efficacy of a periocular recognition framework with respect to the varying acquisition range of images. the framework used in our work is able to achieve a uniform accuracy of 84.3% for all the image acquisition ranges. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Biometrics

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A shock filter for image deblurring and enhancement with oriented hourglass tensor 11

A shock filter for image deblurring and enhancement with ori...

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11th international symposium on image and signal processing and analysis, ispa 2019

作者： Magnier, Baptiste Moradi, Behrang Walbrecq, Cedric IMT Mines Alès LGI2P 6. av. de Clavières Alès30100 France

ISBN: (纸本)9781728131405

this paper presents a new shock filter for image restoration and enhancement by incorporating diffusion information coming for an hourglass filter. Shock filter represents an important family in the field of non-linear Partial Differential Equations (PDEs) models for image restoration and enhancement. Commonly, the smoothed second order derivative of the image assists this type of method in the deblurring mechanism via of non-linear PDEs models. the advantage of the proposed approach is to insert information issued of a non-linear tensor, i.e., the hourglass filter, in a shock filter process. Technically, the non-linear spatial averaging is more precise to enhance images and sharpen contours efficiently simultaneously. the method is compared to 5 other PDE techniques, presenting its interest, robustness and reliability, as, for example, without creating a grainy effect around deblurred edges by visualizing isophote lines. © 2019 IEEE.

关键词： Tensors

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Nonlinear signal and image processing-a special issue in honour of Giovanni L. Sicuranza on his seventy-fifth birthday

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EURASIP JOURNAL ON ADVANCES IN signal processing 2017年第1期2017卷 1-3页

作者： Carini, Alberto Ramponi, Giovanni Mathews, V. John Univ Urbino DiSPeA Piazza Repubbl 13 I-61029 Urbino Italy Univ Trieste DIA Via A Valerio 10 I-34127 Trieste Italy Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA

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Generation and Evaluation of an MRI Statistical Organ Atlas in the Head-Neck Region 10

Generation and Evaluation of an MRI Statistical Organ Atlas ...

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Proceedings of the 10th international symposium on image and signal processing and analysis

作者： Tanacs, Attila Univ Szeged Dept Image Proc & Comp Graph Szeged Hungary

ISBN: (纸本)9781509040117

Segmenting organs in MRI images is a common task in medical practice where image registration techniques can be used in the preprocessing steps to reduce the required interactivity. this is especially true in the head and neck region where large variability of shape and size of organs is present among patients. When an image database of MRI images and segmented organ contours are available, these can be used to build probability atlases in a selected reference frame. the atlas data can then be transformed to the coordinate systems of studies to be segmented applying the transformations in the inverse direction. In this paper two registration approaches for atlas building are evaluated and compared. Separate atlases for 6 organs (spinal cord, trachea, carotis, jugularis, parotis, sternocleidomastoid muscle - SCM) are built from 15 MRI T2 weighted Fast Relaxation Fast Spin Echo (FRFSE) studies using expert segmented organ contours and evaluated using further 15 such studies. the evaluation takes into account the overlap of the expert segmented organ regions and the transformed probability atlases, the discrimination capabilities of the atlases in the carotis-jugularis region, and the errors induced by the inverse registration approach. the results show the superiority of the multiresolution B-Spline transformation implemented by the elastix package against a less flexible, composite transformation formed using scaled rigid + single resolution B-Spline approach. the presented framework can be used for e.g., determining regions of interests (ROIs) as a preprocessing step of learning based fully automatic segmentation approaches.

关键词： Magnetic resonance imaging

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Robust image watermarking using combined DSR-SVM with PCA based feature reduction 6

Robust image watermarking using combined DSR-SVM with PCA ba...

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6th international symposium on Embedded Computing and System Design, ISED 2016

作者： Verma, Vivek Singh Garg, Ajay Kumar Bhardwaj, Anuj Jha, Rajib Kumar Ojha, Aparajita Engineering College Ghaziabad201009 India Jaypee Institute of Information Technology Noida201307 India Indian Institute of Technology Patna800013 India Indian Institute of Information Technology Jabalpur482005 India

ISBN: (纸本)9781509025411

this paper proposes a novel approach for image watermarking using combined dynamic stochastic resonance (DSR) and support vector machine (SVM). the algorithm incorporates lifting wavelet transform (LWT) to decompose the host image into three level frequency sub-bands, and a low-pass frequency sub-band is opted for watermark embedding. Watermark bits are embedded into small blocks of low-pass frequency sub-band using quantization of minimum and maximum coefficients of the corresponding blocks. And, to extract the watermark, DSR based coefficient enhancement process is incorporated. A features set of enhanced block coefficients is generated by employing different statistical parameters, and principal component analysis (PCA) is employed to reduce the dimensions of the features set, which are used for training and testing the learning machine. Training and testing patterns are generated using concatenation of reduced features with enhanced coefficients of the corresponding blocks. Finally, a binary watermark can be extracted using well trained machine (SVM) using binary classification approach. the experimental results show remarkable performance in terms of robustness against various geometrical and signal processing attacks. © 2016 IEEE.

关键词： Support vector machines

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