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Performance Analysis of DWT Families

Performance Analysis of DWT Families

引用

International conference on Intelligent Sustainable Systems (ICISS)

作者： Owais Rashid Asdaq Amin Mohd Rafi Lone School of Engineering and Technology Baba Ghulam Shah Badshah University Rajouri J&K India

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

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.

关键词： Discrete wavelet transforms Transforms wavelet analysis Continuous wavelet transforms Time-frequency analysis Two dimensional displays Transform coding

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Approximating JPEG 2000 wavelet Representation through Deep Neural Networks for Remote Sensing image Scene Classification 25

Approximating JPEG 2000 Wavelet Representation through Deep ...

引用

conference on image and signal processing for Remote Sensing XXV

作者： Byju, Akshara Preethy Sumbul, Gencer Demir, Beguem Bruzzone, Lorenzo Univ Trento Via Sommar 18 I-38123 Povo Trento Italy Tech Univ Berlin Einsteinufer 17 D-10587 Berlin Germany

ISBN: (数字)9781510630147

ISBN: (纸本)9781510630147

This paper presents a novel approach based on the direct use of deep neural networks to approximate wavelet sub-bands for remote sensing (RS) image scene classification in the JPEG 2000 compressed domain. The proposed approach consists of two main steps. The first step aims to approximate the finer level wavelet sub-bands. To this end, we introduce a novel Deep Neural Network approach that utilizes the coarser level binary decoded wavelet sub-bands to approximate the finer level wavelet sub-bands (the image itself) through a series of deconvolutional layers. The second step aims to describe the high-level semantic content of the approximated wavelet sub-bands and to perform scene classification based on the learnt descriptors. This is achieved by: i) a series of convolutional layers for the extraction of descriptors which models the approximated sub-bands;and ii) fully connected layers for the RS image scene classification. Then, we introduce a loss function that allows to learn the parameters of both steps in an end-to-end trainable and unified neural network. The proposed approach requires only the coarser level wavelet sub-bands as input and thus minimizes the amount of decompression applied to the compressed RS images. Experimental results show the effectiveness of the proposed approach in terms of classification accuracy and reduced computational time when compared to the conventional use of Convolutional Neural Networks within the JPEG 2000 compressed domain.

关键词： Scene classification in compressed domain JPEG 2000 deep neural network remote sensing

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Deep Multi-Scale Gabor wavelet Network for image Restoration

Deep Multi-Scale Gabor Wavelet Network for Image Restoration

引用

IEEE International conference on Acoustics, Speech and signal processing

作者： Hang Dong Xinyi Zhang Yu Guo Fei Wang College of Artificial Intelligence Xi’an Jiaotong University School of Software Engineering Xi’an Jiaotong University

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Due to the limitations of the imaging processors and complex weather conditions, image degradation is often inevitable. Existing deep learning-based image restoration methods often rely on the powerful feature representation capacity of deep networks and pay less attention to the inherent properties of the degradation signal, e.g. variations in spatial scale and orientations across the image, which makes them ineffective for the image restoration tasks. In this paper, we propose a Multiscale Gabor wavelet Network (MsGWN) for image restoration. We apply the multi-scale architecture to extract the contaminated feature from input at different spatial scales, and thus the contaminated feature can be effectively restored in a corse- to-fine manner. However, using multi-scale architecture alone cannot remove the degradations with different orientations. To overcome this problem, we introduce a Gabor wavelet Module (GWM) to further extract the contaminated features from four orientations. By decomposing the features into four multi-orientation components, the restoration process can be facilitated by avoiding learning the mixed degradations all-in- one. We evaluate the proposed method on image demoirding, image deraining, and image dehazing. Experiments on these applications demonstrate that the proposed method can achieve favorable results against the state-of-the-art approaches.

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Dual-energy X-ray images enhancement based on a discrete wavelet transform fusion technique for luggage inspection at airport 6

Dual-energy X-ray images enhancement based on a discrete wav...

引用

6th International conference on image and signal processing and their applications, ISPA 2019

作者： Chouai, Mohamed Merah, Mostafa Sancho-Gomez, Jose-Luis Mimi, Malika Mostaganem University Signals and Systems Laboratory Department of Electrical Engineering Mostaganem Algeria Information and Communications Murcia Spain

ISBN: (纸本)9781728131566

Billions of suitcases and other belongings are checked every year in airport X-ray systems around the world. This process is very important because it involves the detection of potentially dangerous objects such as firearms and explosives. However, the image quality of x-ray screening devices needed to be improved. This article attempts to make a contribution to the field of improving the radiographic image of luggage by proposing a complete procedure for improving the image quality based on discrete wavelet transform (DWT) image fusion followed by a noise suppression operation to obtain a good improvement of the fusion result. This article also presents a comparative analysis of five well-known image enhancement algorithm models, the evaluation is done using the Peak signal-to-Noise Ratio (PSNR). The results show that the proposed system provides promising results compared to the other techniques tested, which proves that the proposed approach is very appropriate and recommended for easy / fast identification of threatening objects in a manual or automatic manner. © 2019 IEEE.

关键词： image enhancement

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ENHANCEMENT OF A CNN-BASED DENOISER BASED ON SPATIAL AND SPECTRAL ANALYSIS 26

ENHANCEMENT OF A CNN-BASED DENOISER BASED ON SPATIAL AND SPE...

引用

26th IEEE International conference on image processing (ICIP)

作者： Zhao, Rui Lam, Kin-Man Lun, Daniel P. K. Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781538662496

Convolutional neural network (CNN)-based image denoising methods have been widely studied recently, because of their high-speed processing capability and good visual quality. However, most of the existing CNN-based denoisers learn the image prior from the spatial domain, and suffer from the problem of spatially variant noise, which limits their performance in real-world image denoising tasks. In this paper, we propose a discrete wavelet denoising CNN (WDnCNN), which restores images corrupted by various noise with a single model. Since most of the content or energy of natural images resides in the low-frequency spectrum, their transformed coefficients in the frequency domain are highly imbalanced. To address this issue, we present a band normalization module (BNM) to normalize the coefficients from different parts of the frequency spectrum. Moreover, we employ a band discriminative training (BDT) criterion to enhance the model regression. We evaluate the proposed WDnCNN, and compare it with other state-of-the-art denoisers. Experimental results show that WDnCNN achieves promising performance in both synthetic and real noise reduction, making it a potential solution to many practical image denoising applications.

关键词： image denoising convolutional neural networks spatial-spectral analysis discrete wavelet transform

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Vibration signal Analysis for Bearing Fault Diagnostic of Asynchronous Motor using HT-DWT Technique 6

Vibration Signal Analysis for Bearing Fault Diagnostic of As...

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6th International conference on image and signal processing and their applications, ISPA 2019

作者： Seninete, Sara Mimi, Malika Eddine Cherif, Bilal Djamal Ould Ali, Abdelazziz University Abdel Hamid Ibn Badis of Mostaganem Dept. Electrical Engineering Laboratoire Signaux et Systems Mostaganem Algeria University of Science and Technology of Oran Dept. Electrical Engineering LDEE Laboratory Oran Algeria

ISBN: (纸本)9781728131566

Nowadays, fault detection in induction motors has gained importance. Motor health monitoring is performed to diagnose their operating condition using vibration signals. These signals are processed using different signal processing methods to extract the characteristic parameters permitting localization of the fault. In this paper, we propose a diagnostic method based on Hilbert and Discrete wavelet Transforms for the detection of bearing faults in asynchronous machines. The discrete wavelet transform (DWT) is intended to provide the detail coefficients while the Hilbert transform (HT) is used to obtain the temporal envelope then the envelope spectrum of the detail. The kurtosis value indicates the optimum decomposition wavelet level containing the significant frequencies corresponding to faults for early detection. The result obtained by HT-DWT is more suitable for the analysis of emergency signals. This technique is effective for either stationary or nonstationary signals. Healthy case is compared to faulty case in order to extract frequencies characterizing different faults. The validation of this approach is evaluated by comparing theoretical with experimental results. © 2019 IEEE.

关键词： Induction motors

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Noise power spectrum scene-dependency in simulated image capture systems 17

Noise power spectrum scene-dependency in simulated image cap...

引用

17th image Quality and System Performance conference, IQSP 2020

作者： Fry, Edward W. S. Triantaphillidou, Sophie Jenkin, Robin B. Jacobson, Ralph E. Jarvis, John R. University of Westminster United Kingdom NVIDIA United States

The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply nonlinear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The sceneand-process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-processdependent Modulation Transfer Functions (SPD-MTF). © 2020 Society for Imaging Science and Technology.

关键词： Pipelines

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Veterinary image Enhancement using DWTDCT and Singular Value Decomposition

Veterinary Image Enhancement using DWTDCT and Singular Value...

引用

International conference on Communications and signal processing

作者： Bindhu K Rajan Hiron M Harshan G Venugopal NSS College of Engineering APJ Abdul Kalam Technological University Palakkad Kerala India Gynecology Department College of Veterinary and Animal Sciences Mannuthy Thrissur Kerala India Instrumentation and Control Engineering Department Palakkad APJ Abdul Kalam Technological University Kerala India

ISBN: (数字)9781728149882

ISBN: (纸本)9781728149899

As imaging technology advances, one of the unavoidable part of image processing is the image enhancement. image enhancement is to improve the visual quality for specific applications. The proposed algorithm enhances the visual quality of the vaginoscopic images of bitches using Discrete wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). The input image is decomposed into four frequency bands by using DWT. The low frequency band of the transformed image is again transformed by DCT into low frequency DCT coefficients and higher DCT coefficients. New low frequency band is formed by SVD. Inverse Discrete Cosine Transform (IDCT) is applied to produce the equalized image. Inverse Discrete wavelet Transform (IDWT) of new low frequency band and the other three frequency band are combined to give the enhanced image. Different quality measures such as Root Mean Square Error (RMSE), Peak to signal Noise Ratio (PSNR), Entropy, mean and Standard deviation are used to evaluate the performance of the model. The proposed model is implemented using MATLAB 2017a and obtained an average RMSE of 1.645. PSNR, Entropy, mean and standard deviation values for the proposed model are 44.12dB, 6.57, 103.35, 0.11 respectively. This qualitative analysis shows that proposed method seems to be useful for enhancing veterinary images for further processing.

关键词： image color analysis Entropy Standards Discrete wavelet transforms Discrete cosine transforms image enhancement Imaging

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Staistical features extraction in wavelet domain for texture classification 6

Staistical features extraction in wavelet domain for texture...

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6th International conference on image and signal processing and their applications, ISPA 2019

作者： Zehani, Soraya Ouahabi, Abdeldjalil Mimi, Malika Taleb-Ahmed, Abdelmalik Biskra University Electrical Engineering Department Biskra07000 Algeria University of Tours Polytech Tours Imaging and Brain Tours France Mostaganem University Electrical Engineering Department Mostaganem27000 Algeria UPHF IEMN DOAE UMR CNRS 8520 Valenciennes France

ISBN: (纸本)9781728131566

This paper presents a new approach for texture classification generalizing a well-known statistical features combining the fractal analysis by means of fractal dimension (FD) with the selection first and second order statistics features in the spatial and wavelet domain. The objective of our paper is to propose the features extraction using statistical parameters in the spatial domain and in wavelet domain with different wavelets, with and without preprocessing stage for the texture classification using neural networks for pattern recognition and studying the effect of the preprocessing and wavelets in classification accuracy. The extracted features are used as the input of the ANN classifier. The performance of the proposed methods are evaluated by using two classes of Brodatz database textures. Finally, classification assessment measures such as the confusion matrix, ROC curves and accuracy are applied to the proposed methods. © 2019 IEEE.

关键词： Fractal dimension

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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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