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A secure video watermarking technique based on hyperchaotic Lorentz system

MULTIMEDIA TOOLS AND applications 2019年第18期78卷 26089-26109页

作者： Cao, Zhenlei Wang, Lidan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Natl & Local Joint Engn Lab Intelligent Transmiss Chongqing 400715 Peoples R China Brain Inspired Comp & Intelligent Control Chongqi Chongqing 400715 Peoples R China

The advancement of multimedia and computer technology has facilitated increasing number of research on digital video watermarking. Video watermarking techniques can be used not only in the protection of multimedia video works' copyright, but also in the transmission of confidential data. This paper presents a color video watermarking algorithm based on hyperchaotic Lorentz system. Firstly, the color watermark images are scrambled using hyperchaotic Lorentz system to enhance its confidentiality. Secondly, we use shot boundary detection to extract non-motion frames of the video. Then the chaotic sequence is used to determine the specific frames among the non-motion frames. Next, we apply the discrete wavelet transform to specific frames to get the appropriate subbands. And finally the encrypted watermarks are embedded into the selected subbands. The performance of proposed method is evaluated by Peak signal-to-Noise Ratio, Normalized Correlation and Structural Similarity Index Measure. Experiments showed that the average PSNR and SSIM of watermarked frames are 57.00 dB and 0.99, respectively, which indicate that the proposed method has high imperceptibility. The NC value of 1.00 proves that the watermark can be transmitted without loss under no attacks. And we also tested the robustness and imperceptibility in the presence of various attacks including image processing attacks, geometrical attacks and video attacks. The method we proposed enrich the digital watermarking techniques.

关键词： Hyperchaotic Lorentz system Color image Discrete wavelet transform Video watermark Specific frames

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Denoising of Single-Trial Event-Related Potentials by Shrinkage and Phase Regularization of Analytic wavelet Filterbank Coefficients 9

Denoising of Single-Trial Event-Related Potentials by Shrink...

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9th IEEE/EMBS International Conference on Neural Engineering (NER)

作者： Kohl, Manuel C. Schebsdat, Erik Schneider, Elena N. Strauss, Daniel J. Saarland Univ Hosp Neuroctr Syst Neurosci & Neurotechnol Unit Homburg Germany Saarland Univ Appl Sci Saarbrucken Germany Key Numer GmbH Saarbrucken Germany

ISBN: (纸本)9781538679210

Event-related potentials (ERP) provide reliable electrophysiological correlates of subsequent neural processing following sensory stimulation, offering insight into the activation patterns of participating neural structures which is of considerable value in both neuroscience research and clinical applications. 2D single-trial representations as ERP images have seen increased application in recent studies, accompanied by a rising number of approaches to improve their signal-to-noise ratio, which for the most part have been motivated from an image processing point of view (e.g., nonlocal operators, anisotropic diffusion filtering). In this paper, a brief overview of ERP image denoising prior art is given and a novel, fast denoising algorithm based on split amplitude and phase processing (i.e., phase-informed amplitude shrinkage and regularization of the phase structure) in analytic time-frequency representations of ERP single trials obtained using a perfect reconstruction wavelet filterbank is proposed. Furthermore, the performance of the proposed algorithm is subjected to a comparative evaluation using real-world chirp-evoked auditory ERP acquired from 20 normal hearing adults. Results suggest the suitability of the proposed method for a broad range of a posteriori ERP image denoising tasks, including those lacking a priori knowledge about the shape of potentially nonstationary traces in the ERP image due to, e.g., endogeneous states gradually changing during the experiment.

关键词： Noise reduction Transforms image denoising Chirp Measurement Auditory system signal to noise ratio

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Using wavelet transforms to suppress light interference in the optoelectronic monitoring system of the weft thread break 5

Using wavelet transforms to suppress light interference in t...

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5th International Conference on Innovations in Non-Destructive Testing (SibTest)

作者： Morgunov, Yu Yu Yakunin, A. G. Altai State Tech Univ Barnaul Russia

The article presents the results of studies on interference suppression in optoelectronic methods for monitoring weft thread weaving looms on the basis of linear image sensors caused by inhomogeneities of the background created by sources of natural or artificial light sources in a controlled image scene. Existing solutions that use linear or nonlinear filtering are focused primarily on the processing of two-dimensional images on personal computers and are unsuitable for use in embedded applications. In particular, their use is also ineffective for solving the above problem, when the useful signal is defined as the difference between the output signals of the photodetector taken at a fixed time interval. As studies have shown, in this case, the best result is the use of discrete wavelet - transform.

关键词： Discrete wavelet transforms

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Investigating image applications based on spatial-frequency transform and deep learning techniques

arXiv

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arXiv 2020年

作者： Zheng, Qinkai Qiu, Han Memmi, Gerard Bloch, Isabelle Telecom Paris Institut Polytechnique de Paris Palaiseau91120 France

This is the report for the PRIM project in Telecom Paris. This report is about applications based on spatial-frequency transform and deep learning techniques. In this report, there are two main works. The first work is about the enhanced JPEG compression method based on deep learning. we propose a novel method to highly enhance the JPEG compression by transmitting fewer image data at the sender’s end. At the receiver’s end, we propose a DC recovery algorithm together with the deep residual learning framework to recover images with high quality. The second work is about adversarial examples defenses based on signal processing. We propose the wavelet extension method to extend image data features, which makes it more difficult to generate adversarial examples. We further adopt wavelet denoising to reduce the influence of the adversarial perturbations. With intensive experiments, we demonstrate that both works are effective in their application scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： image compression

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Automated Hyperspectral image Classification Using Spatial-Spectral Features 6

Automated Hyperspectral Image Classification Using Spatial-S...

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6th International Conference on signal processing and Integrated Networks (SPIN)

作者： Dhok, Shivani Bhurane, Ankit Kothari, Ashwin Nagpur IIITN Indian Inst Informat Technol Dept Elect & Commun Engn Nagpur Maharashtra India Visvesvaraya Natl Inst Technol Dept Elect & Commun Engn Nagpur Maharashtra India

ISBN: (纸本)9781728113807

Hyperspectral imaging has transpired as a compelling tool in various fields like geology, milling, agriculture, etc with applications ranging from object detection to quality inspection. Feature extraction, as well as the methodology used for feature extraction, plays an indispensable role in increasing the accuracy of the classification of hyperspectral imaging (HSI). This paper proposes an algorithm for automated hyperspectral image classification using nine spatial-spectral features, which includes linear predictive coefficients, wavelet coefficients, standard deviation, average energy, mean, fractal dimension, entropy, Renyi entropy and Kraskov entropy. These features are further used for classification using the quadratic support vector machine (SVM). The elaborated scheme exercises 10-fold cross-validation. The collective effect of the excerpted features is determined and the accuracy trends for the various number of features is ascertained. Appreciable overall accuracies (OA) for all the three publicly available data sets are acquired as follows: Salinas-A data set (OA = 99.60%), Salinas data set (OA = 92.4%) and Botswana data set (OA = 89.5%).

关键词： hyperspectral imaging linear predictive coefficients wavelet coefficients Kraskov entropy Renyi entropy fractal dimension

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Auto Center Find Density Peaks Algorithm and its Application in Face image Clustering

Auto Center Find Density Peaks Algorithm and its Application...

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2019 IEEE International Conference on signal, Information and Data processing, ICSIDP 2019

作者： Wang, Guiyan Bu, Changjiang Luo, Yuesheng College of Automation Harbin Engineering University Harbin China Harbin Finance University Department of Basic Research Harbin China

ISBN: (纸本)9781728123455

Face image clustering is one of the most important applications in digital signal process. Here, Find Density Peaks clustering algorithm with auto choosing centers is proposed and applied into the face image clustering problem. In the clustering process, the Complex wavelet structural Similarity Index is introduced to quantize the difference between two face images. The deciding mechanism of two key parameters are modified. Avoiding the false centers, the true centers of each cluster are auto defined. And the face images dataset are properly clustered. The face images are dimensionality reduced and visualized via T-SNE as scatter points in 2D space. The results show a good partition of face images dataset. And the proposed algorithm shows practical values in most applications required with clustering. © 2019 IEEE.

关键词： Clustering algorithms

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Underwater image Enhancement by Rayleigh Stretching in Time and Frequency Domain 12

Underwater Image Enhancement by Rayleigh Stretching in Time ...

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12th International Congress on image and signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Peng, Yu-Ting Chang, Chi-Weng Lee, Mong-Shu National Taiwan Ocean University Dept. of Computer Science and Engineering Kee-Lung Taiwan

ISBN: (纸本)9781728148526

image restoration and enhancement play an important role in underwater image applications. How to solve the negative gloomy underwater effects resulting from scattering and color distortion has become the key issues. In this study, we proposed a Rayleigh stretching-based image enhancement method. The method includes a few steps which are background color correction, Rayleigh stretching in time domain, discrete wavelet transform with Contrast Limited Adaptive Histogram Equalization (CLAHE), contrast adjustment, and sharpening. Experimental results through both the qualitative and quantitative analyses demonstrate that our approach greatly improves visibility and contrast and obtains best values with respect to entropy and underwater image quality measure (UIQM), respectively. © 2019 IEEE.

关键词： image enhancement

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Bi-frequency 3D ghost imaging with Haar wavelet transform

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OPTICS EXPRESS 2019年第22期27卷 32349-32359页

作者： Xi, Mengjia Chen, Hui Yuan, Yuan Wang, Gao He, Yuchen Liang, Yan Liu, Jianbin Zheng, Huaibin Xu, Zhuo Xi An Jiao Tong Univ Elect Mat Res Lab Key Lab Minist EducSch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Int Ctr Dielect Res Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Inst Wide Band Gap Semicond Shaanxi Key Lab Informat Photon Tech Xian 710049 Shaanxi Peoples R China

Recently, ghost imaging has been attracting attention because its mechanism could lead to many applications inaccessible to conventional imaging methods. However, it is challenging for high-contrast and high-resolution imaging, due to its low signal-to-noise ratio (SNR) and the demand of high sampling rate in detection. To circumvent these challenges, we propose a ghost imaging scheme that exploits Haar wavelets as illuminating patterns with a bi-frequency light projecting system and frequency-selecting single-pixel detectors. This method provides a theoretically 100% image contrast and high-detection SNR, which reduces the requirement of high dynamic range of detectors, enabling high-resolution ghost imaging. Moreover, it can highly reduce the sampling rate (far below Nyquist limit) for a sparse object by adaptively abandoning unnecessary patterns during the measurement. These characteristics are experimentally verified with a resolution of 512 x 512 and a sampling rate lower than 5%. A high-resolution (1000 x 1000 x 1000) 3D reconstruction of an object is also achieved from multi-angle images. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Computational imaging image processing Imaging techniques Optical imaging Three dimensional imaging X ray imaging

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Proceedings of the 2019 IEEE International Conference on Communication and signal processing, ICCSP 2019

Proceedings of the 2019 IEEE International Conference on Com...

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8th IEEE International Conference on Communication and signal processing, ICCSP 2019

ISBN: (纸本)9781538675953

The proceedings contain 206 papers. The topics discussed include: wavelet based analysis of EEG signal for detecting various conditions of driver;cluster based reliable communication for 5G network;grab force measurement for hand orthosis;design of millimeter wave LC oscillators for 5G applications;a review on multi-model medical image fusion;pattern generation and dimensionality reduction using CNN for image restoration;driver face recognition and sober drunk classification using thermal images;transfer learning with ResNet-50 for malaria cell-image classification;implementation of deep learning algorithm with perceptron using TenzorFlow library;a comparative analysis of machine learning algorithms in detecting deceptive behavior in humans using thermal images;and image segmentation based on Markov random field probabilistic approach.

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A Hybrid Multi-focus image Fusion Technique using SWT and PCA

A Hybrid Multi-focus Image Fusion Technique using SWT and PC...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Tushar Tyagi Parth Gupta Prabhishek Singh Department of CSE Amity University Uttar Pradesh Noida India

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127927

This paper presents a new hybrid and parallel processing image fusion technique for multi-focus images. Here, two different methods are used i.e. Stationary wavelet Transform (SWT) and Principal Component Analysis (PCA) that are implemented on the input images in parallel. These two methods are applied on same input dataset. This method is although computationally bit slower than the compared method but still it shows better results. The fused images obtained from the SWT and PCA are later again fused using PCA method. This is a parallel processing technique. The result of proposed method is compared with other traditional and conventional methods like DWT, SWT and PCA. It is observed that the result of proposed method is better than the compared methods. The result of the proposed method is analyzed qualitatively (visual appearance) and quantitatively using CC (Correlation Coefficient), UIQI (Universal image Quality Index), and PSNR (Peak signal-to-Noise Ratio). The proposed technique will have the capability to be implemented in real time applications of Visual Sensor Network (VSN).

关键词： Conferences Cloud computing Data science Principal component analysis wavelet analysis wavelet transforms

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