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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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wavelet-based image processing for digital mammography

Wavelet-based image processing for digital mammography

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conference on wavelet applications in signal and image processing viii

作者： Qian, W Li, LH Sun, XJ Clarke, RA Univ S Florida Dept Radiol Tampa FL 33612 USA

ISBN: (纸本)0819437646

This paper is to evaluate the importance of image preprocessing using multiresolution and multiorientation wavelet transforms (WTs) on the performance of a previously reported computer assisted diagnostic (CAD) method for breast cancer screening, using digital mammography. An analysis of the influence of WTs on image feature extraction for mass detection is achieved by comparing the discriminate ability of features extracted with and without wavelet based image preprocessing using computed ROC. Three indexes are proposed to assess the segmentation of the mass area with comparison to ground truth. Data was analyzed on region-of-interest (ROI) database that included mass and normal regions from digitized mammograms with ground truth. The metrics for measurement of segmentation of the mass clearly demonstrates the importance of image preprocessing methods. Similarly, the relative improvement in performance is observed in feature extraction, where the Az values are increased. The improvement depends on the feature characteristics. The use of methodology in this paper results in a significant improvement in feature extraction for the previously proposed CAD detection method. We are therefore exploring additional improvement in wavelet based image preprocessing methods, including adaptive methods, to achieve a further improvement in performance on larger image databases.

关键词： digital mammography computer assisted diagnosis wavelet transform multiresolution multiorientation mass detection

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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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Sampling for shift-invariant and wavelet subspaces

Sampling for shift-invariant and wavelet subspaces

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conference on wavelet applications in signal and image processing viii

作者： Hogan, JA Lakey, J Univ Arkansas Dept Math Sci Fayetteville AR 72701 USA

ISBN: (纸本)0819437646

We investigate several issues surrounding the general question of when a function in a finitely generated shift invariant subspace of L-2(R) can be determined by certain of its sample values just as a function bandlimited to [-1/2, 1/2] can be expressed in terms of its integer samples. The main theme here is how answers to this question depend on general properties of the generators of the shift invariant space, such as orthogonality properties, scaling relations, smoothness and so forth. One of the main issues that Re address is the question of how to control aliasing error.

关键词： sampling shift-invariant spaces multiresolution analysis wavelets

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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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Efficient seismic volume compression using the lifting scheme

Efficient seismic volume compression using the lifting schem...

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conference on wavelet applications in signal and image processing viii

作者： Khene, FM Abdul-Jauwad, SH King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

ISBN: (纸本)0819437646

An advanced seismic compression technique is proposed to manage seismic data in a world of ever increasing data volumes in order to maintain productivity without compromising interpretation results. A separable 3-D discrete wavelet transform (DWT) using long biorthogonal filters is used. The computation efficiency of the DWT is improved by factoring the wavelet filters using the lifting scheme. In addition, the lifting scheme offers: 1) a dramatic reduction of the required auxiliary memory, 2) an efficient combination with parallel rendering algorithms to perform arbitrary surface and volume rendering for interactive visualization, and 3) an easy integration in the parallel I/O seismic data loading routines. The proposed technique is tested on a seismic volume from the Stratton field in South Texas. The resulting 3-level multiresolution decomposition yields 21 detail sub-volumes and a unique low-resolution sub-volume. The detail wavelet coefficients are quantized with an adaptive threshold uniform scalar quantizer (TUSQ). The scale-dependent thresholds are determined with the Stein unbiased risk estimate (SURE) principle. As the approximation coefficients represent a smooth low-resolution version of the input data they are only quantized using a uniform scalar quantizer (USQ). Finally a runlength plus a Huffman encoding are applied for binary coding of the quantized coefficients.

关键词： lifting scheme biorthogonal wavelets seismic volume 3-D compression wavelet shrinkage SureShrink

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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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Hierarchical wavelet-based image model for pattern analysis and synthesis

Hierarchical wavelet-based image model for pattern analysis ...

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wavelet applications in signal and image processing viii

作者： Scott, Clayton Nowak, Robert Rice Univ Houston United States

Despite their success in other areas of statistical signal processing, current wavelet-based image models are inadequate for modeling patterns in images, due to the presence of unknown transformations (e.g., translation, rotation, scaling) inherent in most pattern observations. In this paper we introduce a hierarchical wavelet-based framework for modeling patterns in digital images. This framework takes advantage of the efficient image representations afforded by wavelets, while accounting for unknown pattern transformations. Given a trained model, we can use this framework to synthesize pattern observations. If the model parameters are unknown, we can infer them from labeled training data using TEMPLAR (Template Learning from Atomic Representations), a novel template learning algorithm with linear complexity. TEMPLAR employs minimum description length (MDL) complexity regularization to learn a template with a sparse representation in the wavelet domain. We illustrate template learning with examples, and discuss how TEMPLAR applies to pattern classification and denoising from multiple, unaligned observations.

关键词： image analysis

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