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conference on wavelet and Independent Component Analysis applications IX

作者： Labounets, v Maidan, A Labounets-Rundblad, E Astola, J Tampere Univ Technol Tampere Int Ctr Signal Proc FIN-33101 Tampere Finland

ISBN: (纸本)081944488X

Colour image processing is investigated in this paper using an algebraic approach based on triplet numbers. In the algebraic approach, each image element is considered not as a 3D vector, but as a triplet number. The main goal of the paper is to show that triplet algebra can be used to solve colour image processing problems in a natural and effective manner. In this work we propose novel methods for wavelet transforms implementation in colour triplet-valued space.

关键词： wavelets wavelet transforms RGB color model Color image processing 3D image processing Colorimetry Matrix multiplication Fourier transforms Information operations Cones

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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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2 layers bandelets -: Algorithmic interpretation

2 layers bandelets -: Algorithmic interpretation

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conference on wavelet applications in Industrial processing v

作者： Lebrun, Guillaume Carre, Philippe Pateux, Stephane France Telecom Div R&D 4 Rue Clos Courtel F-35512 Cesson Sevigne France SIC F-86962 Futuroscope France

ISBN: (纸本)9780819469236

Nowadays one of the most important issue linked to image transform is to take into account the singularities of a signal which is organized on more than one dimension. The best example is the wavelet transform extension to two dimensional signal analysis. The drawback when one pass from a one dimensional signal process to a two dimensional signal process by simply using separability of wavelet transform is the over representation of irregularities in the wavelet transform domain. In order to decrease this drawback, second generation wavelet transform tries to take geometrical aspects of the image into account in the analysis of the image (one can find examples with bandelets, curvelets, ridgelets and others). 2 layers bandelets or first generation bandelets is among the first wavelet transform which uses the flow to enhance the efficiency of the process. The present proposition is mainly theoretical : we will propose now a pratical interpretation of this work in order to make a new implementation of the transform.

关键词： bandelets denoising geometrical wavelet thresholding

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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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A parametrization technique to design joint time-frequency optimized discrete-time biorthogonal wavelet bases

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signal processing 2017年 135卷 107-120页

作者： Sharma, Manish Achuth, P. v. Pachori, Ram Bilas Gadre, vikram M. Indian Inst Technol Indore Discipline Elect Engn Indore 453552 Madhya Pradesh India Indian Inst Technol Dept Elect Engn Mumbai 400076 Maharashtra India Inst Infrastruct Technol Res & Management IITRAM Dept Elect Engn Ahmadabad Gujarat India

The accurate and efficient representation of a signal in terms of elementary atoms has been a challenge in many signal processing applications including harmonic analysis. The wavelet bases have been proved to be very efficient and flexible atoms. Towards the goal of obtaining optimal wavelet bases, we present a simple and efficient parametrization technique for constructing linear phase biorthogonal discrete-time wavelet bases that have joint time frequency localization (JTFL) close to the lower bound of 0.25. In this paper, we first develop a parametrization technique to design biorthogonal filter banks (FBs). Then an optimization method is formulated to design jointly time frequency localized discrete wavelet bases employing the designed FBs. Finally, the performance of the optimal wavelet bases is evaluated in image coding application. The proposed parametrization method presents a general and yet a very simple framework to construct a linear phase biorthogonal FB of desired order, with the prescribed number of vanishing moments (vMs) and free parameters. Several examples are presented to demonstrate the effectiveness and flexibility of the technique to design different classes of FB with various degrees of freedom. The performance of the designed FBs is compared with the other popular biorthogonal wavelet FBs.

关键词： Biorthogonal wavelet bases Parametrization Joint time-frequency localization image coding

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Speckle filtering of SAR images - A comparative study between complex-wavelet-based and standard filters

Speckle filtering of SAR images - A comparative study betwee...

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conference on wavelet applications in signal and image processing v

作者： Gagnon, L Jouan, A Lockheed Martin Canada (Canada)

ISBN: (纸本)0819425915

We present a comparative study between a complex wavelet Coefficient Shrinkage (WCS) filter and several standard speckle filters that are widely used in the radar imaging community (Lee, Kuan, Frost, Geometric, Kalman, Gamma, etc.). The WCS filter is based on the use of Symmetric Daubechies (SD) wavelets which share the same properties as the real Daubechies wavelets but with an additional symmetry property. The filtering operation is an elliptical soft-thresholding procedure with respect to the principal axes of the 2-D complex wavelet coefficient distributions. Both qualitative and quantitative results (signal to mean square error ratio, equivalent number of looks, edgemap figure of merit) are reported. Tests have been performed using simulated speckle noise as well as real radar images. It is found that the WCS filter performs equally well as the standard filters for low-level noise and slightly outperforms them for higher-level noise.

关键词： image processing synthetic aperture radar speckle wavelets

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An efficient compression scheme based on adaptive thresholding in wavelet domain using particle swarm optimization

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signal processing-image COMMUNICATION 2015年 32卷 33-39页

作者： Ahmadi, Kaveh Javaid, Ahmad Y. Salari, Ezzatollah Univ Toledo Dept EECS Toledo OH 43606 USA

image compression is one of the most important research areas in the field of image processing due to its large number of applications such as aerial surveillance, reconnaissance, medicine and multimedia communication. Even when high data rates are available, image compression is necessary in order to reduce the transmission cost. For applications involving information security, a fast delivery also reduces the chances of compromise over a communication channel. In this paper, we explore the possibility of using one of the computational intelligence techniques, namely, Particle Swarm Optimization (PSO), for optimal thresholding in the 2-D discrete wavelet transform (DWT) of an image. To this end, a set of optimal thresholds is obtained using the PSO algorithm. Finally, a variable length coding scheme, such as arithmetic coding is used to encode the results. Finding an optimal threshold value for the wavelet coefficients is very crucial in reducing the source entropy and bit-rate reduction. The proposed method is tested using several standard images against other popular techniques and proved to be more efficient compared to other methods. (C) 2015 Elsevier B.v. All rights reserved.

关键词： Compression Adaptive thresholding Particle swarm optimization wavelet transform

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The thresholding in calculation algorithm for fast approximation of wavelet transform

The thresholding in calculation algorithm for fast approxima...

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conference on wavelet applications in signal and image processing v

作者： Chang, CC Liu, JC Chan, AK TEXAS A&M UNIV DEPT ELECT ENGNCOLLEGE STNTX 77843

ISBN: (纸本)0819425915

wavelet transform has been widely used in many signal and image processing applications such as edge detection and data compression. For applications that tolerate a slight compromise in accuracy for faster speed, a fast approximation of wavelet transform is favorable. In this paper we propose a simple yet effective algorithm for fast wavelet transform. The use of fixed point numbers simplifies the hardware design and computation complexity than the use of a floating point arithmetic unit. Calculations are further reduced using our thresholding-in-calculations (TIC) technique to omit calculations of small terms that are negligible to the accumulated sum. The TIC technique basically determines whether a multiplication followed by an addition shall be executed using a look-up table with the quantized magnitude of multiplication operands as input parameters. Knowing the levels of quantization, any combination of the quantized multiplication operands can approximate the product and be compared to a predetermined threshold value. If the approximated product is greater than or equal to the threshold value, the corresponding entry in the look-up table is marked for multiplication;otherwise, no multiplication will be executed. Our simulation results show that our approximation algorithm is effective for wavelet transform of audio signals. In addition, when our algorithm is applied to a simple wavelet based edge detection algorithm, the detection result. is almost the same as the one using precise calculation of the wavelet transform.

关键词： fast wavelet transform fixed point numbers thresholding-in-calculations digital filtering edge detection

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Logarithmic dyadic wavelet transform with its applications in edge detection and reconstruction

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APPLIED SOFT COMPUTING 2015年 26卷 193-201页

作者： Tu, Gang Jun Karstoft, Henrik Aarhus Univ Dept Anim Sci DK-8830 Tjele Denmark Aarhus Univ Dept Engn DK-8000 Aarhus C Denmark

In this paper, based on the logarithmic image processing model and the dyadic wavelet transform (DWT), we introduce a logarithmic DWT (LDWT) that is a mathematical transform. It can be used in image edge detection, signal and image reconstruction. Comparative study of this proposed LDWT-based method is done with the edge detection Canny and Sobel methods using Pratt's Figure of Merit, and the comparative results show that the LDWT-based method is better and more robust in detecting low contrast edges than the other two methods. The gradient maps of images are detected by using the DWT- and LDWT-based methods, and the experimental results demonstrate that the gradient maps obtained by the LDWT-based method are more adequate and precisely located. Finally, we use the DWT- and LDWT-based methods to reconstruct one-dimensional signals and two-dimensional images, and the reconstruction results show that the LDWT-based reconstruction method is more effective. (C) 2014 Elsevier B.v. All rights reserved.

关键词： Logarithmic image processing Logarithmic dyadic wavelet transform image edge detection Gradient map signal and image reconstruction

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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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