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Multiscale reconstruction algorithm for compressed sensing

ISA TRANSACTIONS 2014年第4期53卷 1152-1167页

作者： Lei, Jing Liu, Wenyi Liu, Shi Liu, Qibin North China Elect Power Univ Key Lab Condit Monitoring & Control Power Plant E Minist Educ Beijing 102206 Peoples R China Chinese Acad Sci Inst Engn Thermophys Beijing 100190 Peoples R China

Compressed sensing (CS) method has attracted increasing attention owing to providing a novel insight for signal and image processing technology. Acquiring high-quality reconstruction results plays a crucial role in successful applications of CS method. This paper presents a multiscale reconstruction model that simultaneously considers the inaccuracy properties on the measurement data and the measurement matrix. Based on the wavelet analysis method, the original inverse problem is decomposed into a sequence of inverse problems, which are solved successively from the largest scale to the original scale. An objective functional, that integrate the beneficial advantages of the least trimmed sum of absolute deviations (LTA) estimation and the combinational M-estimation, is proposed. An iteration scheme that incorporates the advantages of the homotopy method and the evolutionary programming (EP) algorithm is designed for solving the proposed objective functional. Numerical simulations are implemented to validate the feasibility of the proposed reconstruction method. (C) 2014 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Compressed sensing wavelet analysis Tikhonov regularization method Homotopy method Evolutionary programming method

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Multilevel-DWT based image De-noising using Feed Forward Artificial Neural Network

Multilevel-DWT based Image De-noising using Feed Forward Art...

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1st International Conference on signal processing and Integrated Networks (SPIN)

作者： Saikia, Torali Sarma, Kandarpa Kumar Gauhati Univ Dept Elect & Commun Engn Gauhati 781014 Assam India

ISBN: (纸本)9781479928668

It is seen that during image acquisition, storage, retrieval or transmission, images get degraded due to presence of noise. With different varieties of noise and its extent, de-noising becomes challenging. Traditionally, a host of techniques have considered spatial, statistical and multiple domain approaches for de-noising. Yet, the scope always exist for exploring innovative means of performing de-noising for enhancing image quality. In the proposed work, we present an approach to de-noise images by combining the features of multilevel Discrete wavelet Transform (DWT) and Feed Forward Artificial Neural Network (FF ANN). We apply our algorithm to de-noise the images corrupted by a kind of multiplicative noise known as speckle noise. The results show that the proposed method proves effective for a range of variations and is suitable for critical applications.

关键词： Noise de-noising Multilevel Discrete wavelet Transform (DWT) Feed Forward Artificial Neural Network (FF ANN)

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image ZOOMING USING wavelet COEFFICIENTS

IMAGE ZOOMING USING WAVELET COEFFICIENTS

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作者： Himanshu Jindal THAPAR INSTITUTE OF ENGINEERING AND TECHNOLOGY

学位级别：硕士

The work in this dissertation involves an algorithmic approach to zoom a given image in wavelet domain and to get a sharper image using with various interpolation techniques. The exploration is an attempt to develop quantitative measures that can automatically predict perceived image quality. An objective image quality metric can play a variety of roles in image processing applications. First, it can be used to dynamically monitor and adjust image quality; second, it can be used to optimize algorithms and parameter settings of image processing systems. Second, it can be used to benchmark image processing systems and algorithms as zoomed images are sharper as compared to other methods. Hence keeping all this in mind on this source of information, Discrete wavelet Transform (DWT) with various interpolation techniques had been applied upon variances to obtain their values. Performance is measured by calculating Peak signal to Noise Ratio (PSNR), and the proposed method gives much better PSNR compared to other methods. This algorithm can help in medical science to get the minutest details for detection of cancer.

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wavelet-based polarimetry analysis

Wavelet-based polarimetry analysis

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Geospatial InfoFusion and Video Analytics IV and Motion imagery for ISR and Situational Awareness II

作者： Ezekiel, Soundararajan Harrity, Kyle Farag, Waleed Alford, Mark Ferris, David Blasch, Erik Indiana Univ Penn Indiana PA 15705 USA US Air Force Res Lab Rome NY USA

ISBN: (纸本)9781628410266

wavelet transformation has become a cutting edge and promising approach in the field of image and signal processing. A wavelet is a waveform of effectively limited duration that has an average value of zero. wavelet analysis is done by breaking up the signal into shifted and scaled versions of the original signal. The key advantage of a wavelet is that it is capable of revealing smaller changes, trends, and breakdown points that are not revealed by other techniques such as Fourier analysis. The phenomenon of polarization has been studied for quite some time and is a very useful tool for target detection and tracking. Long Wave Infrared (LWIR) polarization is beneficial for detecting camouflaged objects and is a useful approach when identifying and distinguishing manmade objects from natural clutter. In addition, the Stokes Polarization Parameters, which are calculated from 0 degrees, 45 degrees, 90 degrees, 135 degrees, right circular, and left circular intensity measurements, provide spatial orientations of target features and suppress natural features. In this paper, we propose a wavelet-based polarimetry analysis (WPA) method to analyze Long Wave Infrared Polarimetry imagery to discriminate targets such as dismounts and vehicles from background clutter. These parameters can be used for image thresholding and segmentation. Experimental results show the wavelet-based polarimetry analysis is efficient and can be used in a wide range of applications such as change detection, shape extraction, target recognition, and feature-aided tracking.

关键词： Polarimetry wavelet transform energy Stokes long wave infrared remote sensing

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Visually Significant Feature Point Maps for image Retrieval applications

Visually Significant Feature Point Maps for Image Retrieval ...

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1st International Conference on signal processing and Integrated Networks (SPIN)

作者： Sarathi, M. Partha Ansari, M. A. Amity Univ ASET ECE dept Greater Noida India

ISBN: (纸本)9781479928668

In this paper, we propose a hybrid feature point detector based on a fusion of a multi-scale edge map and a wavelet based interest point representation. The feature points thus obtained are used for efficient image indexing and retrieval. Proposed method preserves an optimum number of significant feature points. Our method is compared against other existing interest point detectors. Efficacy of the proposed method is evaluated via a image retrieval system against the standard test databases. A similarity distance measure like Hausdorff distance is used for image matching, indexing and ranking of results considering the spatial information of the local structures in the image.

关键词： wavelets Canny edge feature points Hausdorff distance Retrieval

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CHANGE DETECTION ON SAR imageS USING DIVISIVE NORMALIZATION-BASED image REPRESENTATION

CHANGE DETECTION ON SAR IMAGES USING DIVISIVE NORMALIZATION-...

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IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Xu, Qian Karam, Lina J. Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85004 USA

ISBN: (纸本)9781479928934

In the context of multi-temporal synthetic aperture radar (SAR) images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or anthropic disaster. In this paper, we propose a new similarity measure for automatic change detection based on a divisive normalization image representation. The divisive normalization transform (DNT) has been recognized as a successful methodology to model the perceptual sensitivity of biological vision and a useful image representation that significantly reduces statistical dependence of natural images. In this work, we exploit the fact that the histogram of DNT coefficients within wavelet subbands can often be well fitted with a zero-mean Gaussian density function, which is a one-parameter function that allows efficient change detection of SAR images. The proposed change detector is compared to other recent model-based approaches. Tests on real data show that our detector outperforms previously suggested methods in terms of the rate of false alarm rate and the total error rate.

关键词： change detection Divisive normalization Gaussian scale mixture synthetic aperture radar (SAR) images

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MatLab acceleration for DWT "Daubechies 9/7" for JPEG2000 Standard on GPU

MatLab acceleration for DWT "Daubechies 9/7" for JPEG2000 St...

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Global Summit on Computer and Information Technology (GSCIT)

作者： Khemiri, Randa Sayadi, Fatma Atri, Mohamed Tourki, Rached Univ Monastir Fac Sci Monastir Lab Elect & Microelect Monastir Tunisia

ISBN: (纸本)9781479956272

Discrete wavelet transform (DWT) has diverse applications in signal and image processing fields. In this paper, we have implemented the lifting "Cohen-Daubechies-Feauveau 9/7" algorithm on a low cost NVIDIA's GPU (Graphics processing Unit) with MatLab to achieve speedup in computation. The efficiency of our GPU based implementation is measured and compared with CPU based algorithms. Our investigational results with GPU show performance enhancement over a factor of 1.82 compared with CPU for an image of size 4096x4096 pixels.

关键词： Discrete wavelet Transform (DWT) Graphics processing unit (GPU) parallel computing MatLab

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3D Dual-Tree Discrete wavelet Transform Based Multiple Description Video Coding 11

3D Dual-Tree Discrete Wavelet Transform Based Multiple Descr...

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11th International Conference on signal processing and Multimedia applications (SIGMAP) is part of 11th International Joint Conference on E-Business and Telecommunications (ICETE)

作者： Chen, Jing Cai, Canhui Li, Li Huaqiao Univ Sch Informat Sci & Engn Xiamen Peoples R China Xiamen Univ Sch Informat Sci & Engn Xiamen Peoples R China

ISBN: (纸本)9789898565969

A 3D dual-tree discrete wavelet transform (DT-DWT) based multiple description video coding algorithm is proposed to combat the transmitting error or packet loss due to Internet or wireless network channel failure. Each description of the proposed multiple description coding scheme consists of a base layer and an enhancement layer. First, the input image sequence is encoded by a standard H.264 encoder in low bit rate to form the base layer, which is then duplicated to each description. Second, the difference between the reconstructed base layer and the input image sequence is encoded by a 3D dual-tree wavelet encoder to produce four coefficient trees. After noise-shaping, these four trees are partitioned into two groups, individually forming enhancement layers of two descriptions. Since the 3D DT-DWT equips 28 directional subbands, the enhancement layer can be coded without motion estimation. The plenty of directional selectivity of DT-DWT solves the mismatch problem and improves the coding efficiency. If all descriptions are available in the receiver, a high quality video can be reconstructed by a central decoder. If only one description is received, a side decoder can be used to reconstruct the source with acceptable quality. Simulation results have shown that the quality of reconstructed video by the proposed algorithm is superior to that by the state-of-the-art multiple description video coding methods.

关键词： Multiple Description Coding Video Coding Dual-Tree Discrete wavelet Transform Layer Coding

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Double-Density Dual-Tree wavelet-based Polarimetry Analysis

Double-Density Dual-Tree Wavelet-based Polarimetry Analysis

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IEEE National Aerospace and Electronics Conference (NAECON)

作者： Harrity, Kyle Ezekiel, Soundararajan Bubalo, Adnan Blasch, Erik Alford, Mark Indiana Univ Penn Indiana PA 15701 USA US Air Force Res Lab Rome NY 13441 USA

ISBN: (纸本)9781479946891

For the past two decades, the Discrete wavelet Transformation (DWT) has been successfully applied to many fields. For image processing applications, the DWT can produce non-redundant representations of an input image with greater performance than other wavelet methods. Further, the DWT provides a better spatial and spectral localization of image representation, capable of revealing smaller changes, trends, and breakdown points that classical methods often miss. However, the DWT has its own limitations and disadvantages such as lack of shift invariance. That is, if the input signal or image is shifted, then the wavelet coefficients will exacerbate that shift. The DWT also lacks the ability to represent directional cases. The Double Density Dual-Tree Discrete wavelet Transformation (D3TDWT) is a relatively new and enhanced version of the DWT with two scaling functions and four distinct wavelets designed in such a way that one pair of wavelets is offset with another pair so that the first pair lies in between the second. In this paper, we propose a D3TDWT polarimetry analysis method to analyze Long Wave Infrared (LWIR) polarimetry imagery to discriminate objects such as people and vehicles from background clutter. The D3TDWT method can be applied to a wide range of applications such as change detection, shape extraction, target recognition, and simultaneous tracking and identification.

关键词： Polarimetry Discrete wavelet Transform Energy Stokes Long Wave Infrared Double Density Dual Tree

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冠脉造影图像序列的时空滤波

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计算机应用 2015年第6期35卷 1734-1738页

作者：周镇镇孙丰荣宋尚玲王丽新栾玉环姚桂华山东大学信息科学与工程学院济南250100 山东大学第二医院设备部济南250033 山东大学齐鲁医院心内科济南250012

为了降低冠脉造影图像的噪声、提高冠心病临床诊断的准确率,达到低X-射线辐射剂量的前提下获得高质量图像的临床应用目的,提出一种冠脉造影图像序列的时空滤波方法。将小波降噪中的阈值降噪思想引入到快速离散正交S变换(FDOST)中,提出... 详细信息

为了降低冠脉造影图像的噪声、提高冠心病临床诊断的准确率,达到低X-射线辐射剂量的前提下获得高质量图像的临床应用目的,提出一种冠脉造影图像序列的时空滤波方法。将小波降噪中的阈值降噪思想引入到快速离散正交S变换(FDOST)中,提出了基于FDOST的软阈值降噪算法对冠脉造影图像进行空域滤波;使用小波降噪对冠脉造影图像序列进行时域滤波,充分利用小波降噪的时间平滑性;加入预处理环节,使用海森矩阵增强冠脉造影图像中的线状结构。仿真及实验结果表明了降噪后的图像信噪比和对比度相比原图像大幅提高,说明了该方法适用于低剂量辐射下的冠脉造影图像序列降噪。

关键词：冠脉造影时空滤波快速离散正交S变换小波变换软阈值降噪算法海森矩阵

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