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Architectures for wavelet transforms: A survey

JOURNAL OF VLSI signal processing SYSTEMS FOR signal image AND VIDEO TECHNOLOGY 1996年第2期14卷 171-192页

作者： Chakrabarti, C Vishwanath, M Owens, RM XEROX CORP PALO ALTO RES CTR COMP SCI LAB PALO ALTO CA 94304 USA PENN STATE UNIV DEPT COMP SCI & ENGN UNIVERSITY PK PA 16802 USA

wavelet transforms have proven to be useful tools for several applications, including signal analysis, signal compression and numerical analysis. This paper surveys the VLSI architectures that have been proposed for computing the Discrete and Continuous wavelet Transforms for I-D and 2-D signals. The architectures are based upon on-line versions of the wavelet transform algorithms. These architectures support single chip implementations and are optimal with respect to both area and time under the word-serial model.

关键词： VLSI circuits

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Construction of fractional spline wavelet bases

Construction of fractional spline wavelet bases

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conference on wavelet applications in signal and image processing vii

作者： Unser, M Blu, T Swiss Fed Inst Technol Biomed Imaging Grp DMT IOA CH-1015 Lausanne Switzerland

ISBN: (纸本)0819432997

We extend Schoenberg's B-splines to all fractional degrees alpha>-1/2. These splines are constructed using linear combinations of the integer shifts of the power functions x(+)(alpha) (one-sided) or \ x \(alpha)(*) (symmetric);in each case, they are alpha-Holder continuous for alpha > 0. They satisfy most of the properties of the traditional B-splines;in particular, the Riesz basis condition and the two-scale relation, which makes them suitable for the construction of new families of wavelet bases;What is especially interesting from a wavelet perspective is that the fractional B-splines have a fractional order o f approximation (alpha + 1), while they reproduce the polynomials of degree [alpha]. We show low they yield continuous-order generalizations of the orthogonal Battle-Lemarie wavelets and of the semi-orthogonal B-spline wavelets. As alpha increases, these latter wavelets tend to be optimally localized in time and frequency in the sense specified by the uncertainty principle. The corresponding analysis wavelets also behave like fractional differentiators;they may therefore be used to whiten fractional Brownian motion processes.

关键词： splines wavelet design fractional derivatives approximation order Strang-Fix theory

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Multiresolution estimation of fractal dimension from images affected by signal-dependent noise

Multiresolution estimation of fractal dimension from images ...

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conference on wavelet applications in signal and image processing vii

作者： Aiazzi, B Alparone, L Baronti, S Garzelli, A CNR IROE Nello Carrara Res Inst Electromagnet Waves I-50127 Florence Italy

ISBN: (纸本)0819432997

A well-suited approach to calculate the fractal dimension of digital images stems from the power spectrum of a fractional Brownian motion: the ratio between powers at different scales is related to the persistence parameter H and, thus, to the fractal dimension D = 3 - H. The signal-dependent nature of the speckle noise, however, prevents from a correct estimation of fractal dimension from Synthetic Aperture Radar (SAR) images. Here, we propose and assess a novel method to obtain D based on the multiscale decomposition provided by the normalized Laplacian pyramid (NLP), which is a bandpass representation obtained by dividing the layers of an LP by its expanded baseband and is designed to force the noise to become signal-independent. Extensive experiments on synthetic fractal textures, both noise-free and noisy, corroborate the underlying assumptions and show the performances, in terms of both accuracy and confidence of estimation, of pyramid methods compared with the well-established method based on the wavelet transform. Preliminary results on true SAR images from ERS-1 look promising as well.

关键词： fractional Brownian motion fractal dimension power spectrum multiresolution analysis wavelet transform Laplacian pyramid Synthetic Aperture Radar (SAR) images speckle texture

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image Restoration by Adaptive wavelet Thresholding Method

Image Restoration by Adaptive Wavelet Thresholding Method

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21st signal processing and Communications applications conference (SIU)

作者： Erdem, Mehmet Can Telatar, Ziya Dogan, Mustafa Ankara Univ Elekt & Elekt Muhendisligi Bolumu TR-06100 Ankara Turkey Dogus Univ Muhendisligi Bolumu Kontrol Otomasyon Istanbul Turkey

ISBN: (纸本)9781467355636;9781467355629

image enhancement and restoration methods are essential for many fields like medical imaging and radar imaging systems. In literature, there are many studies and different approaches to image enhancement and restoration methods. In this paper, some noise models are studied and the performances of Wiener filter, median filter, mean filter and a proposed method based on adaptive wavelet thresholding are compared on images degraded by mentioned noise models.

关键词： image enhancement image restoration noise models adaptive wavelet thresholding

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wavelets in industrial applications: a review

Wavelets in industrial applications: a review

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conference on wavelet applications in Industrial processing II

作者： Truchetet, F Laligant, O Univ Bourgogne F-21004 Dijon France

ISBN: (纸本)0819455601

This paper aims at reviewing the recent published works dealing with industrial applications of wavelet and, more generally speaking, multiresolution analysis. After a quick recall in a simple overview of the basics of wavelet transform and of its main variations, some of its applications are reviewed domain by domain, beginning with signal processing, continuous and discrete wavelet transform proceeding with image processing and applications. More than 150 recent papers are presented in these two sections.

关键词： wavelets wavelet transforms signal processing Discrete wavelet transforms image compression image processing Analytical research Feature extraction Statistical analysis Video compression

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image denoising using neighbouring wavelet coefficients

Image denoising using neighbouring wavelet coefficients

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IEEE International conference on Acoustics, Speech, and signal processing

作者： Chen, GY Bui, TD Krzyzak, A Concordia Univ Dept Comp Sci Montreal PQ H3G 1M8 Canada

The denoising of a natural image corrupted by Gaussian noise is a classical problem in signal or image processing. Donoho and his coworkers at Stanford pioneered a wavelet denoising scheme by thresholding the wavelet coefficients arising from the standard discrete wavelet transform. This work has been widely used in science and engineering applications. However, this denoising scheme tends to kill too many wavelet coefficients that might contain useful image information. In this paper, we propose one wavelet image thresholding scheme by incorporating neighbouring coefficients, namely NeighShrink. This approach is valid because a large wavelet coefficient will probably have large wavelet coefficients as its neighbours. Experimental results show that NeighShrink is better than the Wiener filter and the conventional wavelet denoising approaches: VisuShrink and SUREShrink. We also investigate different neighbourhood sizes and find that a size of 3 x 3 is the best among all window sizes.

关键词： image processing

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Dual Tree Complex wavelet Transform Based Speech Enhancement 23

Dual Tree Complex Wavelet Transform Based Speech Enhancement

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23nd signal processing and Communications applications conference (SIU)

作者： Tasmaz, Haci Adiyaman Univ Muhendisl Fak Elekt Elekt Muhendisligi Bolumu Adiyaman Turkey

ISBN: (纸本)9781467373869

Dual tree complex wavelet transform (DT-CWT) has the advantages of nearly shift-invariance and directional selectivity (for two or more dimensions) over the classical discrete wavelet transform. These advantages are essential for many signal processing applications (i.e. image fusion, image enhancement, pattern recognition). In his study, a speech enhancement method based on the DT-CWT has been proposed in order to test its performance in speech enhancement. An efficient estimator, multiplicatively modified log-spectral amplitude (MM-LSA) estimator is used for the enhancement of noisy subband wavelet coefficients. The objective and experimental results show the superiority of the proposed method to the wavelet transform based methods well known in the literature.

关键词： speech enhancement discrete wavelet transform wavelet packet transform lifting wavelet transform stationary wavelet transform dual tree complex wavelet transform multiplicatively modified log-spectral amplitude estimator

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wavelet-based pavement distress detection and evaluation

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OPTICAL ENGINEERING 2006年第2期45卷

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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image enhancement method based on wavelet transform

Image enhancement method based on wavelet transform

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conference on Hybrid image and signal processing vii

作者： Xu, B Fu, CY Ma, JG Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China

ISBN: (纸本)0819436704

The wavelet transform theory emerged in recent years has been applied in many fields, such as image compression, edge and feature detection, image enhancement and texture analysis. We have studied some of the developments that lead to the current state of image enhancement, sufficiently considering the configurations of video image and eyespot requirement. This paper presents a new method to accentuate image using multi-scale analysis, time-frequency wavelet transform and minimum distance criteria. After decomposing an image into components of different size, position, and orientation, the amplitude of coefficients in the wavelet transform domain can be altered prior to obtaining the inverse transform. This can selectively accentuate interesting components at the expense of undesirable ones. Experimental results have shown that simpler algorithm, faster running speed, rather improved SNR and contrast can be obtained with this method, resulting in a good enhancement effect.

关键词： wavelet Transform (WT) Multi-resolution image enhancement signal Noise Ratio (SNR) Fast Fourier Transform (FFT)

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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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