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IEEE International conference on Acoustics, Speech, and signal processing

作者： Xiao, S Kozintsev, I Ramchandran, K Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)0780362934

In this work, we introduce an efficient hidden Markov field model for wavelet image coefficients and apply it to the image denoising problem. Specifically, we propose to model wavelet image coefficients within subbands as Gaussian random variables with parameters determined by underlying hidden Markov-type process. Our model is inspired by the recent Estimation-Quantization image coder [1] and the work in [2]. To reduce the computational complexity we apply the novel factor graph [3] framework to combine two 1-D chain models to approximate a hidden Markov field (HMF) model. We then apply the proposed models for wavelet image coefficients to perform an approximate Minimum Mean Square Error (MMSE) estimation procedure to restore an image corrupted by additive white Gaussian noise. Our results are among the state-of-the-art in the field and they indicate the promise of the proposed modeling techniques.

关键词： image denoising

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wavelet-based pavement distress detection and evaluation

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OPTICAL ENGINEERING 2006年第2期45卷 027007-1-027007页

作者： Zhou, J Huang, PS Chiang, FP SUNY Stony Brook Dept Mech Engn Stony Brook NY 11794 USA

An automated pavement inspection system consists of image acquisition and distress image processing. The former is accomplished with imaging sensors, such as video cameras and photomultiplier tubes. The latter includes distress detection, isolation, classification, evaluation, segmentation, and compression. We focus on wavelet-based distress detection, isolation, and evaluation. After a pavement image is decomposed into different-frequency subbands by the wavelet transform, distresses are transformed into high-amplitude wavelet coefficients and noise is transformed into low-amplitude wavelet coefficients, both in the high-frequency subbands, referred to as details. Background is transformed into wavelet coefficients in a low-frequency subband, referred to as approximation. First, several statistical criteria are developed for distress detection and isolation, which include the high-amplitude wavelet coefficient percentage (HAWCP), the high-frequency energy percentage (HFEP), and the standard deviation (STD). These criteria are tested on hundreds of pavement images differing by type, severity, and extent of distress. Experimental results demonstrate that the proposed criteria are reliable for distress detection and isolation and that real-time distress detection and screening is currently feasible. A norm for pavement distress quantification, which is defined as the product of HAWCP and HFEP, is also proposed. Experimental results show that the norm is a useful index for pavement distress evaluation. (c) 2006 Society of Photo-Optical Instrumentation Engineers.

关键词： wavelet transform automated pavement inspection pavement distress image processing distress detection and isolation distress evaluation

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Natural image Deblurring using Recursive Deep Convolutional Neural Network (R-DbCNN) and Second-Generation wavelets

Natural Image Deblurring using Recursive Deep Convolutional ...

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IEEE International conference on signal and image processing applications (IEEE ICSIPA)

作者： Khmag, Asem Kamarudin, Noraziahtulhidayu Univ Zawia Dept Comp Syst Engn Fac Engn Az Zawia Libya Univ Coll Technol Sarawak Sch Comp & Creat Sarawak Malaysia

ISBN: (纸本)9781728133775

Deconvolution in blind digital images is a common issue in image enhancement techniques, which basically was a notion of many researches. In this study, spatial varying blind deconvloution is stated and implemented. In addition, image noise removal approach which utilizes the normalized platform of second-generation wavelet transform is applied as pre-processing step. The low and high frequencies are decomposed in this step in order to be extracted. Practically, the main merit of wavelet transform is its efficiency in reduction of data redundancy in digital images. This feature helps a lot in terms of data classification where it is easy to distinguish the signal from its noisy counterpart. The second step, a recursive deep convolutional neural network (R-DbCNN) is implemented to suppress any image blur affected by second-generation wavelet transform to further remove the blur of noisy image. The experimental results depict that the suggested method outperforms recent blur removal techniques for different bluer image types in terms of image quality and time consumption.

关键词： image deblurring second-generation wavelet transforms image enhancement convolution neural network image denoising

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Artifact free signal denoising with wavelets

Artifact free signal denoising with wavelets

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IEEE International conference on Acoustics, Speech, and signal processing

作者： Durand, S Froment, J CMLA Ecole Normale Super Cachan F-94235 Cachan France

ISBN: (纸本)0780370414

Recent years have seen the development of signal denoising algorithms based on wavelet transform. It has been shown that thresholding the wavelet coefficients of a noisy signal allows to restore the smoothness of the original signal. However, wavelet denoising suffers of a main drawback : around discontinuities the reconstructed signal is smoothed, exhibiting pseudo-Gibbs phenomenon. We consider the problem of denoising piecewise smooth signals with sharp discontinuities. We propose to apply a traditional wavelet denoising method and to restore the denoised signal using a total variation minimization approach. This second step allows to remove the Gibbs phenomena and therefore to restore sharp discontinuities, while the other structures are preserved. The main innovation of our algorithm is to constrain the total variation minimization by the knowledge of the remaining wavelet coefficients. In this way, we make sure that the restoration process does not deteriorate the information that has been considered as significant in the denoising step. With this approach we substantially improve the performance of classical wavelet denoising algorithms, both in terms of SNR and in terms of visual artifacts.

关键词： signal processing

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Decomposition and compression of Kirchhoff migration operator by adapted wavelet packet transform

Decomposition and compression of Kirchhoff migration operato...

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conference on wavelet applications in signal and image processing vi

作者： Wang, YZ Wu, RS Univ Calif Santa Cruz Inst Tecton Santa Cruz CA 95064 USA

ISBN: (纸本)0819429139

Kirchhoff migration operator is a highly oscillatory integral operator. In our previous work (see "Seismic Imaging in wavelet Domain", Wu and Yang [1], 1997), we have shown that the matrix representation of Kirchhoff migration operator for homogeneous background in space-frequency domain is a dense matrix, while the compressed beamlet-operator, which is the wavelet decomposition of the Kirchhoff migration operator in beamlet-frequency (space-scale-frequency) domain, is a highly sparse matrix. Using the compressed matrix for imaging, we can obtain high quality images with high efficiency. We found that the compression ratio of the migration operator is very different for different wavelet basis. In the present work, we study the decomposition and compression of Kirchhoff migration operator by adapted wavelet packet transform, and compare with the standard discrete wavelet transform (DWT). We proposed a new maximum sparsity adapted wavelet packet transform (MSAWPT), which differs from the well-known Coifman-Wickerhauser's best basis algorithm, to implement the decomposition of Kirchhoff operator to achieve the maximum possible sparsity. From the numerical tests, it is found that the MSAWPT can generate a more efficient matrix representation of Kirchhoff migration operator than DWT and the compression capability of MSAWPT is much greater than that of DWT.

关键词： adapted wavelet packet transform best basis wavelet transform operator decomposition operator compression compression ratio Kirchhoff migration operator

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Bridging scale-space to multiscale frame analyses

Bridging scale-space to multiscale frame analyses

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IEEE International conference on Acoustics, Speech, and signal processing

作者： Bao, YF Krim, H N Carolina State Univ ECE Dept Raleigh NC 27695 USA

ISBN: (纸本)0780370414

We address a well known problem of nonlinear image diffusion techniques, namely the loss of texture information. We do so by first determining that it is due to unaccounted correlation structure in the image which we subsequently mitigate by proposing a wavelet frame-based technique. This, by the same token establishes a theoretical bridge between the scale space methodology and the multiscale analysis approach. We provide examples to illustrate the effectiveness of the proposed approach.

关键词： image processing

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wavelet analysis of the timing of individual components of the ECG signal

Wavelet analysis of the timing of individual components of t...

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IEEE International conference on Acoustics, Speech, and signal processing

作者： Burke, MJ Nasor, M Trinity Coll Dublin Dept Elect & Elect Engn Dublin 2 Ireland

ISBN: (纸本)0780370414

ECG recordings were obtained from 21 healthy subjects aged between 13 and 65 years, over a range of heart rate extending from 46 to 184 beats/min (bpm). A wavelet transform method, based on the Mexican Hat wavelet was then used to precisely locate the positions of the onset, peak and termination of individual components in the ECG signal. These times were then classified according to the heart rate associated with the cardiac cycle to which the component belonged. Second order equations in the square root of the cardiac cycle time, TR-R Of the form ***(R-R) + ***-R + C were fitted to the data obtained for each component to characterize its timing variation.

关键词： Electrocardiography electrochemical grinding wavelet Analysis Heart Rate Healthy Volunteers Square root signals precise registration vital Signs

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Beamspace Bearing Estimation Based on wavelet Transform

Beamspace Bearing Estimation Based on Wavelet Transform

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3rd IEEE International conference on signal and image processing applications (ICSIPA)

作者： Du, Jinxiang Ma, Yan Northwestern Polytech Univ Coll Marine Xian 710072 Shaanxi Provinc Peoples R China

ISBN: (纸本)9781479902699;9781479902675

In this paper, we propose a new wideband bearing estimation method based on wavelet transform. By analyzing the relationship between the wavelet transform of the frequency invariant beam's output and the array's beampattern, we derived spatial power spectrum based on wavelet transform (SPS-WT). The method has good performance on noise suppression by utilizing the statistical uncorrelation character between signals and noise, and also has high resolution on bearing estimation. The performance of the proposed method is illustrated in simulation results.

关键词： array signal processing direction-of-arrival estimation statistical analysis wavelet transforms

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Adapted filter banks in machine learning: applications in biomedical signal processing

Adapted filter banks in machine learning: Applications in bi...

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IEEE International conference on Acoustics, Speech, and signal processing

作者： Strauss, DJ Delb, W Jung, J Plinkert, PK Key Numer Saarbrucken Germany

ISBN: (纸本)0780376633

The theory of signal-adapted filter banks has been developed in signal compression in recent years and only rarely be applied to other applications fields such as machine learning. In this paper, we propose lattice structure based signal-adapted filter banks and time-scale atoms, respectilvely, for the construction of morphological local discriminant bases and hybrid wavelet-support vector classifiers. The first mentioned method is a more powerful construction of the recently introduced local discriminant bases algorithm which employs, additionally to the conventional wavelet-packet tree adjustment, an adaptation of the analyzing time-scale atoms. The latter mentioned method utilizes adapted wavelet decompositions which are tailored for support vector classifiers with radial basis functions as kernels. For both methods, we present applications in biomedical signal processing.

关键词： Biomedical engineering

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Industrial applications of the wavelet and multiresolution based signal-image processing, a review

Industrial applications of the wavelet and multiresolution b...

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8th International conference on Quality Control by Artificial vision

作者： Truchetet, Fred Laligant, Olivier Univ Bourgogne CNRS Le2i UMR 5158 F-21004 Dijon France

ISBN: (纸本)9780819464514

25 years after the seminal work of Jean Morlet, the wavelet transform, multiresolution analysis, and other space frequency or space scale approaches are considered standard tools by researchers in image processing, and many applications have been proposed that point out the interest of these techniques. This paper proposes a review of the recent published works dealing with industrial applications of wavelet and, more generally speaking, multiresolution analysis. More than 180 recent papers are presented.

关键词： wavelets multiresolution multiscale signal processing image processing industrial applications

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