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Trainlets: Dictionary Learning in High Dimensions

IEEE TRANSACTIONS ON signal processing 2016年第12期64卷 3180-3193页

作者： Sulam, Jeremias Ophir, Boaz Zibulevsky, Michael Elad, Michael Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

Sparse representation has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance. Combined with the ability to learn a dictionary from signal examples, sparsity-inspired algorithms are often achieving state-of-the-art results in a wide variety of tasks. These methods have traditionally been restricted to small dimensions mainly due to the computational constraints that the dictionary learning problem entails. In the context of image processing, this implies handling small image patches. In this work we show how to efficiently handle bigger dimensions and go beyond the small patches in sparsity-based signal and image processing methods. We build our approach based on a new cropped wavelet decomposition, which enables a multi-scale analysis with virtually no border effects. We then employ this as the base dictionary within a double sparsity model to enable the training of adaptive dictionaries. To cope with the increase of training data, while at the same time improving the training performance, we present an Online Sparse Dictionary Learning (OSDL) algorithm to train this model effectively, enabling it to handle millions of examples. This work shows that dictionary learning can be up-scaled to tackle a new level of signal dimensions, obtaining large adaptable atoms that we call Trainlets.

关键词： Double-sparsity K-SVD dictionary learning cropped wavelet on-line learning trainlets contourlets

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Moving shadow detection based on stationary wavelet transform

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EURASIP JOURNAL ON image AND VIDEO processing 2017年第1期2017卷 1页

作者： Nagarathinam, Kavitha Kathavarayan, Ruba Soundar Saranathan Coll Engn CSE Dept Tiruchirappalli Tamil Nadu India PSR Engn Coll CSE Dept Sivakasi Tamil Nadu India

Many surveillance and forensic applications face problems in identifying shadows and their removal. The moving shadow points overlap with the moving objects in a video sequence leading to misclassification of the exact object. This article presents a novel method for identifying and removing moving shadows using stationary wavelet transform (SWT) based on a threshold determined by wavelet coefficients. The multi-resolution property of the stationary wavelet transform leads to the decomposition of the frames into four different bands without the loss of spatial information. The conventional discrete wavelet transform (DWT), which has the same property, suffers from the problem of shift invariance due to the decimation operation leading to a shift in the original signal during reconstruction. Since SWT does not have the decimation operation, the problem of shift invariance is solved which makes it feasible for change detection, pattern recognition and feature extraction and retrieves the original signal without the loss of phase information also. For detection and removal of shadow, a new threshold in the form of a variant statistical parameter-"skewness"-is proposed. The value of threshold is determined through the wavelet coefficients without the requirement of any supervised learning or manual calibration. Normally, the statistical parameters like mean, variance and standard deviation does not show much variation in complex environments. Skewness shows a unique variation between the shadow and non-shadow pixels in various environments than the previously used thresholds-standard deviation and relative standard deviation. The experimental results prove that the proposed method works better than other state-of-art-methods.

关键词： Object tracking Shadow detection Skewness SWT Video forensics and surveillance wavelet transform

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A Fast Discrete wavelet Transform Using Hybrid Parallelism on GPUs

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2016年第11期27卷 3088-3100页

作者： Tran Minh Quan Jeong, Won-Ki UNIST Dept Elect & Comp Engn Ulsan South Korea

wavelet transform has been widely used in many signal and image processing applications. Due to its wide adoption for time-critical applications, such as streaming and real-time signal processing, many acceleration techniques were developed during the past decade. Recently, the graphics processing unit (GPU) has gained much attention for accelerating computationally-intensive problems and many solutions of GPU-based discrete wavelet transform (DWT) have been introduced, but most of them did not fully leverage the potential of the GPU. In this paper, we present various state-of-the-art GPU optimization strategies in DWT implementation, such as leveraging shared memory, registers, warp shuffling instructions, and thread-and instruction-level parallelism (TLP, ILP), and finally elaborate our hybrid approach to further boost up its performance. In addition, we introduce a novel mixed-band memory layout for Haar DWT, where multi-level transform can be carried out in a single fused kernel launch. As a result, unlike recent GPU DWT methods that focus mainly on maximizing ILP, we show that the optimal GPU DWT performance can be achieved by hybrid parallelism combining both TLP and ILP together in a mixed-band approach. We demonstrate the performance of our proposed method by comparison with other CPU and GPU DWTmethods.

关键词： wavelet transform hybrid parallelism lifting scheme bit rotation GPU computing

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Double Density Dual Tree Discrete wavelet Transform implementation for Degraded image Enhancement

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Journal of Physics: Conference Series 2018年第1期1000卷

作者： C Vimala P Aruna Priya Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur India

wavelet transform is a main tool for image processing applications in modern existence. A Double Density Dual Tree Discrete wavelet Transform is used and investigated for image denoising. images are considered for the analysis and the performance is compared with discrete wavelet transform and the Double Density DWT. Peak signal to Noise Ratio values and Root Means Square error are calculated in all the three wavelet techniques for denoised images and the performance has evaluated. The proposed techniques give the better performance when comparing other two wavelet techniques.

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Improving mesh-based motion compensation by using edge adaptive graph-based compensated wavelet lifting for medical data sets

Improving mesh-based motion compensation by using edge adapt...

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Daniela Lanz Andre Kaup Multimedia Communications and Signal Processing Friedrich-Alexander-Universitat Erlangen-Nurnberg (FAU) Cauerstr. 7 91058 Germany

ISBN: (纸本)9781509041183

Medical applications like Computed Tomography (CT) or Magnetic Resonance Tomography (MRT) often require an efficient scalable representation of their huge output volumes in the further processing chain of medical routine. A downscaled version of such a signal can be obtained by using image and video coders based on wavelet transforms. The visual quality of the resulting lowpass band, which shall be used as a representative, can be improved by applying motion compensation methods during the transform. This paper presents a new approach of using the distorted edge lengths of a mesh-based compensated grid instead of the approximated intensity values of the underlying frame to perform a motion compensation. We will show that an edge adaptive graph-based compensation and its usage for compensated wavelet lifting improves the visual quality of the lowpass band by approximately 2.5 dB compared to the traditional mesh-based compensation, while the additional filesize required for coding the motion information doesn't change.

关键词： Discrete wavelet Transforms Motion Compensation Scalability signal processing on Graphs Computed Tomography Motion compensation discrete wavelet transform Scalability X-Ray Computed Tomography compensated wavelets video coder lift lifts Edge of a figure or solid

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image compression algorithm using wavelet transform 39

Image compression algorithm using wavelet transform

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Conference on applications of Digital image processing XXXIX

作者： Cadena, Luis Cadena, Franklin Simonov, Konstantin Zotin, Alexander Okhotnikov, Grigory Univ Fuerzas Armadas ESPE Av Gral Ruminahui S-N Sangolqui Ecuador Colegio Juan Suarez Chacon Quito Ecuador Inst Computat Modelling SB RAN 50-44 Akademgorodok Str Krasnoyarsk Russia Siberian State Aerosp Univ 31 Krasnoyarsky Rabochy Pr Krasnoyarsk Russia St Petersburg State Univ Univ Skaya Embankment 7-9 St Petersburg Russia

ISBN: (数字)9781510603349

ISBN: (纸本)9781510603332;9781510603349

Within the multi-resolution analysis, the study of the image compression algorithm using the Haar wavelet has been performed. We have studied the dependence of the image quality on the compression ratio. Also, the variation of the compression level of the studied image has been obtained. It is shown that the compression ratio in the range of 8-10 is optimal for environmental monitoring. Under these conditions the compression level is in the range of 1.7 - 4.2, depending on the type of images. It is shown that the algorithm used is more convenient and has more advantages than Winrar. The Haar wavelet algorithm has improved the method of signal and image processing.

关键词： signal processing image processing Haar wavelet wavelet transform compression ratio

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Fast cloud detection using low-frequency components of satellite imagery

Fast cloud detection using low-frequency components of satel...

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IEEE signal processing and Communications applications (SIU)

作者： D. Utku Ufuk Caner Demirpolat M. Fatih Demirci Bilgisayar Mühendisliği Bölümü TOBB ETÜ Ankara Türkiye TÜBİTAK Uzay Teknolojileri Araştırma Enstitüsü Ankara Türkiye

ISBN: (纸本)9781509064953

Being able to rapidly detect clouds in satellite imagery is critical in terms of increasing mission efficiency of ground observation satellites. In this work, a new approach has been proposed for cloud detection involving the utilisation of low-frequency Discrete wavelet Transform (DWT) components whose sizes are 1/64 of the original image sizes. In the proposed method, several texture features are calculated from the low-frequency DWT components and the cloud pixels are detected by using a K-Nearest Neighbors (KNN) classifier. The proposed method has been tested on the Göktürk-2 images. Since the utilization of low-frequency components leads to a significant loss in detail; the cloud detection performance have decreased to some extent. Nevertheless the obtained results were found to be sufficient for determining the cloudiness rate at a small margin of error, and at approximately 145× increased processing speed.

关键词： Satellites Remote sensing Earth Discrete wavelet transforms Feature extraction Art

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Noise reduction using wavelet cycle spinning: analysis of useful periodicities in the z-transform domain

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signal image AND VIDEO processing 2016年第3期10卷 519-526页

作者： Rodriguez-Hernandez, Miguel A. San Emeterio, Jose L. Univ Politecn Valencia ITACA E-46022 Valencia Spain CSIC ITEFI Sensors & Ultrason Syst Dept Madrid Spain

Cycle spinning (CS) and a'trous algorithms are different implementations of the undecimated wavelet transform (UWT). Both algorithms can be used for UWT and even though the resulting wavelet coefficients are different, they keep a correspondence. This paper describes an analysis of the CS algorithm performed in the z-transform domain, showing the similarities and differences with the a'trous implementation. CS generates more wavelet coefficients than a'trous, but the number of significative and different coefficients is the same in both cases because of the occurrence of a periodic repetition in CS coefficients. Mathematical expressions for the relationship between CS and a'trous coefficients and for CS coefficient periodicities are provided in the z-transform domain. In some wavelet denoising applications, periodicities (present in the coefficients of the CS procedure) can also be found in the performance measure of the processed signals. In particular, in ultrasonic CS denoising applications, periodicities have been appreciated in the signal-to-noise ratio (SNR) of the ultrasonic denoised signals. These periodicities can be used to optimize the number of CS coefficients for an efficient implementation. Two examples showing the periodicities in the SNR are included. A selection of several reduced sets of CS wavelet coefficients has been utilized in the examples, and the SNRs resulting after denoising are analyzed.

关键词： wavelets Cycle spinning Periodicities signal denoising Ultrasonics Z-transform

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Quaternion wavelet transform for image denoising

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Journal of Physics: Conference Series 2018年第1期974卷

作者： Ahmad Khairul Umam Mahmud Yunus Mathematics Department Institut Teknologi Sepuluh Nopember Sukolilo Surabaya 60111 Indonesia

Quaternion wavelet transform (QWT) combines discrete wavelet transform (DWT) and quaternion Fourier transform (QFT). QWT has many applications included image processing. In this research, we discuss about construction, characteristics and implementation of QWT on process of image denoising. We construct denoising algorithm with QWT then we do simulation to know performance of algorithm. We use grayscale test images that have size 512 × 512 pixel with low, medium and high complexity. Experiment removes noise of image successfully. Results of image denoising are used to measure algorithm performance using PSNR (peak signal to noise ratio) value. We compare PSNR values with DWT and QWT for Haar, Biorthogonal, Daubechies and Coiflets wavelet. The method that has the highest PSNR value can be concluded the best performance.

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Analysis and Performance Optimization of a GPGPU Implementation of image Quality Assessment (IQA) Algorithm VSNR

Analysis and Performance Optimization of a GPGPU Implementat...

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作者： Gupta, Ayush Arizona State University

学位级别：M.S.

image processing has changed the way we store, view and share images. One important component of sharing images over the networks is image compression. Lossy image compression techniques compromise the quality of images to reduce their size. To ensure that the distortion of images due to image compression is not highly detectable by humans, the perceived quality of an image needs to be maintained over a certain threshold. Determining this threshold is best done using human subjects, but that is impractical in real-world scenarios. As a solution to this issue, image quality assessment (IQA) algorithms are used to automatically compute a fidelity score of an image. However, poor performance of IQA algorithms has been observed due to complex statistical computations involved. General Purpose Graphics processing Unit (GPGPU) programming is one of the solutions proposed to optimize the performance of these algorithms. This thesis presents a Compute Unified Device Architecture (CUDA) based optimized implementation of full reference IQA algorithm, Visual signal to Noise Ratio (VSNR) that uses M-level 2D Discrete wavelet Transform (DWT) with 9/7 biorthogonal filters among other statistical computations. The presented implementation is tested upon four different image quality databases containing images with multiple distortions and sizes ranging from 512 x 512 to 1600 x 1280. The CUDA implementation of VSNR shows a speedup of over 32x for 1600 x 1280 images. It is observed that the speedup scales with the increase in size of images. The results showed that the implementation is fast enough to use VSNR on high definition videos with a frame rate of 60 fps. This work presents the optimizations made due to the use of GPU’s constant memory and reuse of allocated memory on the GPU. Also, it shows the performance improvement using profiler driven GPGPU development in CUDA. The presented implementation can be deployed in production combined with existing applications.

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