Adaptive Wavelet Shrinkage Research Articles

An adaptive wavelet shrinkage based accumulative frame differencing model for motion segmentation

Motion segmentation in real-world scenes is a fundamental component in computer vision. There exists a variety of motion recognition algorithms, each with varying degrees of accuracy and computational complexity. The most widely used techniques, in the case of static cameras, are those based on frame difference. Those methods have a significant weakness when it comes to detect slow moving objects. Therefore, we introduce in this paper a novel approach that aims to improve motion segmentation by proposing an accumulative wavelet based frame differencing technique. Moreover, in the proposed approach we exploit a combination of several techniques to efficiently enhance the quality of motion segmentation results. The approach's performance on real-world video sequences shows that comparing frames using the 2D wavelet transform increases motion segmentation quality.

Domain-independent severely noisy image segmentation via adaptive wavelet shrinkage using particle swarm optimization and fuzzy C-means

Noisy image segmentation is a hot topic in natural, medical, and remote sensing image processing. It is among the non-trivial problems of computer vision having to address denoising and segmentation at the same time. Fuzzy C-means (FCM) is a clustering algorithm that has been shown to be effective at dealing with both segmentation-oriented denoising and segmentation at the same time. Moreover, with a high level of noise and other imaging artifacts, FCM loses its ability to perform image segmentation effectively. This paper introduces a Particle Swarm Optimization (PSO)-based feature enhancement approach in the wavelet domain for noisy image segmentation. This approach applies adaptive wavelet shrinkage using FCM clustering performance as an evaluation mechanism and also as the segmentation algorithm. The PSO-based process helps to enhance intensity features for clustering-based denoising, and also provides adaptivity for the system that performs well on a range of real, synthetic, and simulated noisy images with different noise levels and range/spatial properties. Furthermore, the algorithm applies edge enhancement based on Canny edge detector in order to further improve accuracy. Experiments are presented using three different datasets each degraded with different types of common noise. The presented algorithms show effective and consistent performance over a range of severe noise levels without the need for any parameter tuning.

Segmentation improvement through denoising of PET images with 3D-context modelling in wavelet domain

Positron emission tomography (PET) images have been incorporated into the radiotherapy process as a powerful tool to assist in the contouring of lesions, leading to the emergence of a broad spectrum of automatic segmentation schemes for PET images (PET-AS). However, not all proposed PET-AS algorithms take into consideration the previous steps of image preparation. PET image noise has been shown to be one of the most relevant affecting factors in segmentation tasks. This study demonstrates a nonlinear filtering method based on spatially adaptive wavelet shrinkage using three-dimensional context modelling that considers the correlation of each voxel with its neighbours. Using this noise reduction method, excellent edge conservation properties are obtained. To evaluate the influence in the segmentation schemes of this filter, it was compared with a set of Gaussian filters (the most conventional) and with two previously optimised edge-preserving filters. Five segmentation schemes were used (most commonly implemented in commercial software): fixed thresholding, adaptive thresholding, watershed, adaptive region growing and affinity propagation clustering. Segmentation results were evaluated using the Dice similarity coefficient and classification error. A simple metric was also included to improve the characterisation of the filters used for induced blurring evaluation, based on the measurement of the average edge width. The proposed noise reduction procedure improves the results of segmentation throughout the performed settings and was shown to be more stable in low-contrast and high-noise conditions. Thus, the capacity of the segmentation method is reinforced by the denoising plan used.

A Novel Edge Preserving Improved Adaptive Wavelet Filter (EPIAWF) for Speckle Removal in Ultrasound Images

Background: The paper proposes a new method for improving the quality of ultrasound medical images using a two stage wavelet based denoising approach. The first stage utilizes an adaptive wavelet shrinkage function with a novel interscale measure based variance estimate and a new subband adaptive threshold for reducing the speckle present in ultrasound images. Methods: The performance of the first stage is further improved in the second by employing an error measure estimate. Thus the proposed denoising approach is optimized in the mean square error sense and results in improved noise removal as well as in the edge preservation of the reconstructed image. It also improves the visual quality of the ultrasound images. Next, quantitative evaluation is also done with performance metrics like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM), Equivalent Number of Looks (ENL) and Edge preservation Index (EPI). Results: The performance of the proposed approach is measured and compared with the existing approaches and found to be better in improving the quality of ultrasound images. Keywords: Ultrasound images, speckle, adaptive filter, undecimated, wavelet, edge preservation.

Hybridization of Adaptive Wavelet Shrinkage with Guided Filter for Speckle Reduction in Ultrasound Images

Speckle noise is introduced in ultrasound images during acquisition and it is inherited in all coherent sources. Speckle makes area of interest difficult to study for human observer and for automated diagnostic machines hence the interpretation may adversely affect the results. Numerous post processing de-speckling algorithms have been developed but along with speckle reduction the challenging task is to preserve edges or evincive detail. In this study, authors have developed a hybrid method using adaptive wavelet shrinkage and guided filter which has given us remarkable results in de-speckling and edge preservation. In the paper proposed method is compared with existing filters like Lee, Kuan, Frost, Guided filter and wavelet shrinkage techniques Visu Shrink, Bayes Shrink and Normal Shrink. The results of proposed technique were visually better than existing de-speckling techniques. The quantitative analysis also shows eminence of proposed technique over existing techniques when compared using different image quality metrics.

Denoising of PET images by context modelling using local neighbourhood correlation

Positron emission tomography (PET) images are characterised by low signal-to-noise ratio and blurred edges when compared with other image modalities. It is therefore advisable to use noise reduction methods for qualitative and quantitative analyses. Given the importance of the maximum and mean uptake values, it is necessary to avoid signal loss, which could modify the clinical significance. This paper proposes a method of non-linear image denoising for PET. It is based on spatially adaptive wavelet-shrinkage and uses context modelling, which explicitly considers the correlation between neighbouring pixels. This context modelling is able to maintain the uptake values and preserve the edges in significant regions. The algorithm is proposed as an alternative to the usual filtering that is performed after reconstruction.

An Integrated De-Speckling Approach for Medical Ultrasound Images Based on Wavelet and Trilateral Filter

Speckle noise is a granular artifact in medical ultrasound images. It causes serious problems and hinders the development of automatic diagnosis technology. A novel and integrated approach is proposed to address the de-noising problem by using wavelet transformation and trilateral filter. Firstly, a dynamic additive model is developed to account for the medical ultrasound signal with speckle noise.Secondly, in accordance with the statistical property of the additive model, an adaptive wavelet shrinkage algorithm is applied to the noisy medical signal. Particularly, the algorithm is significant to the high-frequency component of the speckle noise in the wavelet domain. Thirdly, but most importantly, the low-frequency component of the speckle noise is suppressed by a trilateral filter. It simultaneously reduces the speckle and impulse noise in real set data. Finally, a lot of experiments are conducted on both synthetic images and real clinical ultrasound images for authenticity. Experimental results show the proposed method not only guarantees noise reduction, but also performswell on edge sharpening and offers greater flexibility. Compared with other existingmethods, experimental results show that the proposed algorithm demonstrates an excellent de-noising performance, offers great flexibility and substantially sharpens the desirable edge.

An Integrated De-speckling Approach for Medical Ultrasound Images Based on Wavelet and Trilateral Filter

Speckle noise is a granular artifact in medical ultrasound images. It causes serious problems and hinders the development of automatic diagnosis technology. In this paper, a novel and integrated approach is proposed to address the de-noising problem by using wavelet transformation and trilateral filter. Firstly, a dynamic additive model is developed to account for the medical ultrasound signal with speckle noise. Secondly, in accordance with the statistical property of the additive model, an adaptive wavelet shrinkage algorithm is applied to the noisy medical signal. Particularly, the algorithm is significant to the high-frequency component of the speckle noise in the wavelet domain. Thirdly, but most importantly, the low-frequency component of the speckle noise is suppressed by a trilateral filter. It simultaneously reduces the speckle and impulse noise in real set data. Finally, a lot of experiments are conducted on both synthetic images and real clinical ultrasound images for authenticity. Compared with other existing methods, experimental results show that the proposed algorithm demonstrates an excellent de-noising performance, offers great flexibility and substantially sharpens the desirable edge.

An improved adaptive wavelet shrinkage for ultrasound despeckling

Ultrasound imaging is the most widely used medical diagnostic technique for clinical decision making, due to its ability to make real time imaging for moving structures, low cost and safety. However, its usefulness is degraded by the presence of signal dependent speckle noise. Several wavelet-based denoising schemes have been reported in the literature for the removal of speckle noise. This study proposes a new and improved adaptive wavelet shrinkage in the translational invariant domain. It exploits the knowledge of the correlation of the wavelet coefficients within and across the resolution scales. A preliminary coefficient classification representing useful image information and noise is performed with a novel inter-scale dependency measure. The spatial context adaptation of the wavelet coefficients within a subband is achieved by a local spatial adaptivity indicator, determined by using a truncation threshold. A weighted signal variance is estimated based on this measure and used in the determination of a subband adaptive threshold. The proposed thresholding function aims to reduce the fixed bias of the soft thresholding approach. Experiments conducted with the proposed filter are compared with the existing filtering algorithms in terms of Peak-Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Measure (SSIM), Equivalent Number of Looks (ENL) and Edge Preservation Index (EPI). A comparison of the results shows that the proposed filter achieves an improvement in terms of quantitative measures and in terms of visual quality of the images.

Adaptive wavelet shrinkage for noise robust speaker recognition

Speaker recognition faces many practical difficulties, among which signal inconsistency due to environmental and acquisition channel factors is most challenging. The noise imposed to the voice signal varies greatly and a priori noise model is usually unavailable. In this article, we propose a robust speaker recognition method that employs a novel adaptive wavelet shrinkage method for noise suppression. In our method, wavelet subband coefficient thresholds are automatically computed, which are proportional to the noise contamination. In the application of wavelet shrinkage for noise removal, a dual-threshold strategy is developed to suppress noise, preserve signal coefficients and minimize the introduction of artifacts. The recognition is achieved using modification of Mel-frequency cepstral coefficient of overlapped voice signal segments. The efficacy of our method is evaluated with voice signals from two public available speech signal databases and is compared with state-of-the-art methods. It is demonstrated that our proposed method exhibits great robustness in various noise conditions. The improvement is significant especially when noise dominates the underlying speech.

Denoising of Mechanical Vibration Signals Using Quantum-Inspired Adaptive Wavelet Shrinkage

The potential application of a quantum-inspired adaptive wavelet shrinkage (QAWS) technique to mechanical vibration signals with a focus on noise reduction is studied in this paper. This quantum-inspired shrinkage algorithm combines three elements: an adaptive non-Gaussian statistical model of dual-tree complex wavelet transform (DTCWT) coefficients proposed to improve practicability of prior information, the quantum superposition introduced to describe the interscale dependencies of DTCWT coefficients, and the quantum-inspired probability of noise defined to shrink wavelet coefficients in a Bayesian framework. By combining all these elements, this signal processing scheme incorporating the DTCWT with quantum theory can both reduce noise and preserve signal details. A practical vibration signal measured from a power-shift steering transmission is utilized to evaluate the denoising ability of QAWS. Application results demonstrate the effectiveness of the proposed method. Moreover, it achieves better performance than hard and soft thresholding.

Spatially adaptive antialiasing for enhancement of mobile imaging system using combined wavelet-fourier transform

This paper presents an adaptive anti-aliasing algorithm based on the wavelet-Fourier transform and directionally adaptive wavelet shrinkage. The proposed antialiasing algorithm detects aliasing artifacts by analyzing the frequency characteristics of discrete wavelet transform (DWT) coefficients, and then removes the aliasing artifacts by shrinking the transform coefficients in the directionally adaptive manner. More specifically, the proposed algorithm analyzes the property of LH, HL, and HH subbands of the DWT, and reduces aliasing artifacts in the LL subband by shrinking the coefficients in the patch-based adaptive manner. On the other hand, aliasing artifacts in LH, HL, and HH subbands are reduced using a directional filtering. The resulting anti-aliased image is obtained by taking the inverse DWT. Experimental results show that the proposed algorithm can efficiently reduce the aliasing artifacts while preserving the high-frequency image details. The proposed anti-aliasing algorithm is suitable for not only mobile imaging systems but also various application areas of image restoration and multidimensional sampling.

A MODIFIED ADAPTIVE WAVELET SHRINKAGE SPECKLE FILTER FOR ULTRASOUND IMAGES

The usage of ultrasound imaging for medical diagnosis is limited due to the presence of speckle noise. In this study Modified Adaptive Wavelet Shrinkage Filter (MAWSF) in the translational invariant domain is proposed for the removal of speckle noise. The adaptive wavelet threshold function removes the fixed bias of soft thresholding. A new inter-scale dependency model is proposed, to perform a primary clustering of signal of interest and noise. Then, anew sub-band adaptive threshold is determined for all high frequency sub-bands at various decomposition levels, to shrink the noisy coefficients. Experiment is conducted on several ultrasound scan images. The results show that this method yields better visual quality and Peak Signal to Noise Ratio (PSNR). Improvement in preservation of edge details is also found measured with Edge Preservation Index (EPI) measure.

Doppler Signal Denoising Based on Feature Adaptive Wavelet Shrinkage Method

The envelope extraction of Doppler signal spectrum is very important in ultrasonic blood flow detection, due to the fact that it can provide the diagnosis information of blood circulatory system. Doppler signals are often polluted by noises, which will affect the performance of the envelope extraction. Therefore, it is necessary to remove the noises before extracting the spectrum envelope. In this paper, a Doppler denoising method based on the Feature Adaptive Wavelet Shrinkage is proposed. The advantage of this method is that the threshold of each coefficient is set by using the coefficient at the current location and its two neighbor coefficients. Simulation results demonstrate that the proposed method can remove the noises of Doppler signals more effectively compared to the traditional wavelet threshold method.

An Adaptive Wavelet Shrinkage and its Application in Image De-Noising

In this paper an adaptive method of shrinkage of the wavelet coefficients is presented. In the method, the wavelet coefficients are divided into two classes by a threshold. One class of them with the smaller absolute values at a scale is transformed with a proportional relation，another class with the larger absolute values at the same scale is transformed with a linear function. The threshold and the coefficient in the proportional relation or in the linear function are determined by the principle of minimizing the Stein’s unbiased risk estimate. In the paper, the method of estimation of the threshold and the coefficient is given and the adaptive method of shrinkage of the wavelet coefficients is applied to image denoising. Examples in the paper show that the presented method has an advantage over SureShrink from the point of view of both the Stein’s unbiased risk estimate and the signal-to-noise ratio. In addition, the method takes almost the same computing time as the SureShrink in image denoising.

Denoising Nonlinear Time Series by Adaptive Filtering and Wavelet Shrinkage: A Comparison

Time series measured in real world is often nonlinear, even chaotic. To effectively extract desired information from measured time series, it is important to preprocess data to reduce noise. In this Letter, we propose an adaptive denoising algorithm. Using chaotic Lorenz data and calculating root-mean-square-error, Lyapunov exponent, and correlation dimension, we show that our adaptive algorithm more effectively reduces noise in the chaotic Lorenz system than wavelet denoising with three different thresholding choices. We further analyze an electroencephalogram (EEG) signal in sleep apnea and show that the adaptive algorithm again more effectively reduces the Electrocardiogram (ECG) and other types of noise contaminated in EEG than wavelet approaches.

An adaptive wavelet shrinkage approach to the Spektor–Lord–Willis problem

The stereological problem of unfolding the sphere size distribution from linear sections is considered. A minimax estimator of the intensity function of a Poisson process that describes the problem is introduced and an adaptive estimator is constructed that achieves the optimal rate of convergence over Besov balls to within logarithmic factors. The construction of these estimators uses Wavelet–Vaguelette Decomposition (WVD) of the operator that defines our inverse problem.

A Real-Time Wavelet-Domain Video Denoising Implementation in FPGA

The use of field-programmable gate arrays (FPGAs) for digital signal processing (DSP) has increased with the introduction of dedicated multipliers, which allow the implementation of complex algorithms. This architecture is especially effective for data-intensive applications with extremes in data throughput. Recent studies prove that the FPGAs offer better solutions for real-time multiresolution video processing than any available processor, DSP or general-purpose. FPGA design of critically sampled discrete wavelet transforms has been thoroughly studied in literature over recent years. Much less research was done towards FPGA design of overcomplete wavelet transforms and advanced wavelet-domain video processing algorithms. This paper describes the parallel implementation of an advanced wavelet-domain noise filtering algorithm, which uses a nondecimated wavelet transform and spatially adaptive Bayesian wavelet shrinkage. The implemented arithmetic is decentralized and distributed over two FPGAs. The standard composite television video stream is digitalized and used as a source for real-time video sequences. The results demonstrate the effectiveness of the developed scheme for real-time video processing.

Extracting burst and tonic components from surface electromyograms in dystonia using adaptive wavelet shrinkage

The compound surface electromyograms (EMGs) recorded from patients with dystonia commonly contains superimposed bursting and tonic activity representing various motor symptoms. It is desirable to differentially extract them from the compound EMGs so that different symptoms can be more specifically investigated and different mechanisms revealed. A non-linear denoising approach based on wavelet transformation was investigated by applying soft thresholding to the wavelet coefficients. Thresholds were determined according to three different principles and two models. Different techniques for wavelet shrinkage were investigated for separating burst and tonic activity in the compound EMGs. The combination of Stein’s unbiased risk estimate principle with a non-white noise model proved optimal for separating burst and tonic activity. These turned out to be exponentially related; and the temporal relationships between antagonist muscle contractions could now be seen clearly. We conclude that adaptive soft-thresholding wavelet shrinkage provides effective separation of burst and tonic activity in the compound EMG in dystonia. This separation should improve our understanding of the pathophysiology of dystonia.

Design of nonlinear adaptive digital filter by wavelet shrinkage

AbstractThe wavelet shrinkage method is a nonlinear signal denoising procedure to remove noise by shrinking the empirical wavelet coefficients in the wavelet domain. In this paper, we apply the wavelet shrinkage method to nonlinear adaptive digital filters, and we propose the adaptive wavelet shrinkage filter which updates the threshold levels adaptively by the LMS algorithm according to the reference signal. The adaptive wavelet shrinkage filter is robust and has the property that one can control the filter easily by modifying the threshold levels. We describe the theorems concerning the convergence of the LMS algorithm for updating the threshold levels, and demonstrate several examples of the adaptive wavelet shrinkage filter. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(2): 59–71, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10032