Wavelet Domain Filtering Research Articles

Interferometric phase diagram generation method based on dot trace texture information and semantic segmentation of correlation coefficients

This paper proposes a method for generating interferometric phase diagrams, which includes interferometric image registration based on Dot Trace Texture Information (DTTI) and interferometric phase filtering based on semantic segmentation. First, the InSAR images are sharpened according to the different backscattering characteristics of various pixel values, and DTTI is extracted to generate the Dot Trace Texture Map (DTTM). Second, SIFT feature points are extracted from two InSAR coherent complex images. They are then screened by comparison with DTTI. These selected points are used to calculate the registration relationship, ensuring precise registration of the two images. Third, the interferometric phase diagram is segmented by semantics based on correlation coefficients. For high-quality interferometric phase regions, we use two-dimensional frequency domain rotational symmetric Gaussian Laplace and wavelet domain filters successively. For low-quality regions, we use an average filter to smooth out noise. The superiority of the proposed algorithm was verified through comparative experiments.

Enhancement and labelling of OCT images

Abstract Optical Coherence Tomography (OCT) is a noninvasive technique for visualizing retinal cross-sections, assisting in diagnosing and monitoring retinal diseases. This study presents an effective OCT image noise reduction and segmentation method. Due to the high resolution of our primary image database, a visual comparison of filter performance was challenging. To address this, we utilized a high-noise image dataset from Duke University, enabling a better evaluation of noise reduction filters. Our method was compared against eight widely-used image filters, including Gaussian, Low pass,Wavelet domain filtering, Lee filter, Anisotropic diffusion, Bilateral filter, Total variational filter, and BM3D filter. Both visual and quantitative analyses were conducted using no-reference performance parameters, namely Gradient Magnitude Similarity Deviation (GMSD), Standard Deviation of Wavelet Coefficients (SDWC), Focus Measure with Tengrade Variance (FMTV), Perception-based Image Quality Evaluator (PIQE), and Visual Information Fidelity (VIF). The results demonstrated the superiority of our proposed filter in terms of noise reduction performance while maintaining the sharpness of retinal layers. Quantitative analysis revealed notable performance gains, including improvements of 63.27% to 83.24% with GMSD, consistent edge strength enhancement of 9.4% to 9.97% using SDWC, gains in image quality between 51.99% and 54.64% with FMTV, performance improvements ranging from 7.25% to 23.97% in terms of PIQE, and a substantial increase in performance varying from 16.31% to 27.69% when assessing the impact on retinal layer quality using VIF. Using the Kruskal-Wallis test, our proposed noise reduction method shows statistical significance for all quantitative parameters. Additionally, our clustering algorithm effectively separated the foreground, including retinal layers and vitreous detachment, from the background and identified an area representing the region between retinal layers where fluid accumulates. We’ve successfully achieved OCT image enhancement, along with distinct foreground and background segmentation.

Motion-Frames Based Video Watermarking Scheme for Copyright Protection Using Guided Filtering in Wavelet Domain

Motion-Frames Based Video Watermarking Scheme for Copyright Protection Using Guided Filtering in Wavelet Domain

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters

An image enhancement method based on gabor filtering in wavelet domain and adaptive histogram equalization

<p><span>The images are not always good enough to convey the proper information. The image may be very bright or very dark sometime or it may be low contrast or high contrast. Because of these reasons image enhancement plays important role in digital image processing. In this paper we proposed an image enhancement technique in which Gabor and median filtering is performed in wavelet domain and Adaptive Histogram Equalization is performed in spatial domain. Brightness and contrast are the two parameters used for analyzing the performance of the proposed method</span></p>

The method to capture the form of opera performance based on machine vision

In order to overcome the problems of traditional opera performance form capture methods, such as long denoising time and low capture accuracy, this paper proposes a new opera performance form capture method based on machine vision. The wavelet domain and gradient domain are obtained by multi-step local Wiener filtering in wavelet domain. The parameters of wavelet-based Wiener filter are modified to obtain a new wavelet threshold function. The grey images of different components of different opera performance form images are extracted for comparison, and the best component image is selected for threshold processing according to different opera performance forms. At the same time, image features are extracted in the form of binary image to realise the opera performance form capture based on machine vision. Experimental results show that the proposed method can quickly and accurately capture opera performance form, and the highest capture efficiency can reach 99.55%.

Hybrid Method for Detection Tumor using Genetic Algorithm and Swarm Optimization after Wavelet Domain Filtering Then uing Marr-Hilerth

Hybrid Method for Detection Tumor using Genetic Algorithm and Swarm Optimization after Wavelet Domain Filtering Then uing Marr-Hilerth

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images.

Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two-dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub-band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub-band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.

Variational mode decomposition based ECG denoising using non-local means and wavelet domain filtering.

This paper presents a novel electrocardiogram (ECG) denoising approach based on variational mode decomposition (VMD). This work also incorporates the efficacy of the non-local means (NLM) estimation and the discrete wavelet transform (DWT) filtering technique. Current ECG denoising methods fail to remove noise from the entire frequency range of the ECG signal. To achieve the effective ECG denoising goal, the noisy ECG signal is decomposed into narrow-band variational mode functions (VMFs) using VMD method. The idea is to filter out noise from these narrow-band VMFs. To achieve that, the center frequency information associated with each VMFs is used to exclusively divide them into lower- and higher-frequency signal groups. The higher frequency VMFs were filtered out using DWT-thresholding technique. The lower frequency VMFs are denoised through NLM estimation technique. The non-recursive nature of VMD enables the parallel processing of NLM estimation and DWT filtering. The traditional DWT-based approaches need large decomposition levels to filter low frequency noises and at the same time NLM technique suffers from the rare-patch effect in high-frequency region. On the contrary, in the proposed framework both NLM and DWT approaches complement each other to overcome their individual ill-effects. The signal reconstruction is performed using the denoised high frequency and low frequency VMFs. The simulation performed on the MIT-BIH Arrhythmia database shows that the proposed method outperforms the existing state-of-the-art ECG denoising techniques.

Hue preserving‐based approach for underwater colour image enhancement

In this study, a novel underwater colour image enhancement approach based on hue preserving is presented by combining hue-saturation-intensity (HSI) and HS-value (HSV) colour models. In this study, the proposed wavelet-domain filtering (WDF) and constrained histogram stretching (CHS) algorithms are operated on HSI and HSV colour models, respectively. The degraded image is first converted from red-green-blue colour model into the HSI colour model, wherein the hue component H is preserved and WDF algorithm is executed on the S and I components. Similarly, the image is further converted into the HSV colour model, wherein H component is kept invariant as well and CHS algorithm is applied on the S and V components. The authors' key contribution is that the H preserving method can improve image quality in terms of contrast, colour rendition, non-uniform illumination, and denoising. In addition, experimental results show that the proposed approach outperforms several other state-of-the-art algorithms.

A comparison of methods for denoising of well test pressure data

Pressure transient data from downhole gauges are one of the key parameters in characterizing reservoir properties and forecasting future reservoir performance. Reservoir pressure is usually measured under dynamic changes. The collected data usually contain different levels of noise, particularly due to imperfections in measuring instruments and imperfect calibrations. The latter is due to changes between the laboratory environment and reservoir conditions. To have accurate descriptions of reservoir, it is essential to smooth the pressure data. Most related studies have employed the wavelet transform to reduce noise. However, there appears to be little research addressing the use of other smoothing techniques for pressure transient data. This paper, therefore, evaluates and compares the performance of three types of smoothing and noise removal methods, namely wavelet transform as a widely used filtering technique, regression-based smoothers, and autoregressive smoothing methods to reduce artificial noise added to simulated dual-porosity pressure data. Particularly, noise is more pronounced in pressure derivative, and so denoising of pressure derivative requires more effective tools. The effectiveness of the noise removing methods was compared using mean square error. The results show that the regression-based methods lead to the same or even better reduction in the noise level as compared to the wavelet domain filter, while the employed autoregressive method results in a moderate performance. We also test the performance of various combinations of the different smoothing methods to filter the same noisy data. It is shown that the combined locally weighted scatterplot smooth (LOESS) and autoregressive moving average (ARMA) gives the best smoothing performance for pressure derivative data. Application of the combined LOESS–ARMA to real field data shows promising results.

A review on echocardiographic image speckle reduction filters

Speckle noise is a granular disturbance, which is an inherent property in echocardiographic images. This usually degrades the image resolution and contrast. The image with speckle noise is very difficult to interpret. Despeckling is necessary to improve the visual quality for better diagnoses. In last thirty years, different methods have been proposed for despeckling of echocardiographic images. In this paper a review of these techniques is done highlighting the different types of filters and evolving approaches over last three decades. The types of filters are mainly divided into linear filers, diffusion filters and wavelet domain filters. The main aim of this paper is to do comparative study of different thresholding techniques in wavelet domain.

Image Denoising by Median Filter in Wavelet Domain

The details of an image with noise may be restored by removing noise through a suitable image de-noising method. In this research, a new method of image de-noising based on using median filter (MF) in the wavelet domain is proposed and tested. Various types of wavelet transform filters are used in conjunction with median filter in experimenting with the proposed approach in order to obtain better results for image de-noising process, and, consequently to select the best suited filter. Wavelet transform working on the frequencies of sub-bands split from an image is a powerful method for analysis of images. According to this experimental work, the proposed method presents better results than using only wavelet transform or median filter alone. The MSE and PSNR values are used for measuring the improvement in de-noised images.

Resolution enhancement in tilted coordinates

Deconvolution is applied to remove source wavelet effects from seismograms. The results are resolution enhancement that enables detection of thin layers. Following enhancement of resolution, low frequency and high angle reflectors, particularly at great depth, appear as low amplitude and semi-invisible reflectors that are difficult to track and pick. A new approach to enhance resolution is introduced that estimates a derivative using continuous wavelet transform in tilted coordinates. The results are compared with sparse spike deconvolution, curvelet deconvolution and inverse quality filtering in wavelet domain. The positive consequence of the new method is to increase sampling of high dip features by changing the coordinate system from Cartesian to tilted. To compare those methods a complex data set was chosen that includes high angle faults and chaotic mass transport complex. Image enhancement using curvelet deconvolution shows a chaotic system as a non-chaotic one. The results show that sparse spike deconvolution and inverse quality filtering in wavelet domain are able to enhance resolution more than curvelet deconvolution especially at great depth but it is impossible to follow steep dip reflectors after resolution enhancement using these methods, especially when their apparent dips are more than 45°. By estimating derivatives in a continuous wavelet transform from tilted data sets similar resolution enhancement as the other deconvolution methods is achieved but additionally steep dipping reflectors are imaged much better than others. Subtracted results of the enhanced resolution data set using new method and the other introduced methods show that steeply dipping reflectors are highlighted as a particular ability of the new method. The results show that high frequency recovery in Cartesian co-ordinate is accompanied by inability to image steeply dipping reflectors especially at great depths. Conversely recovery of high frequency data and imaging of the data both at shallow and great depth are an ability of resolution enhancement in tilted co-ordinates.

Denoising of natural images through robust wavelet thresholding and genetic programming

Digital images play an essential role in analysis tasks that can be applied in various knowledge domains, including medicine, meteorology, geology, and biology. Such images can be degraded by noise during the process of acquisition, transmission, storage, or compression. The use of local filters in image restoration may generate artifacts when these filters are not well adapted to the image content as a result of the heuristic optimization of local filters. Denoising methods based on learning procedure are more capable than parametric filters for addressing the conflicts between noise suppression and artifact reduction. In this study, we present a nonlinear filtering method based on a two-step switching scheme to remove both salt-and-pepper and additive white Gaussian noises. In the switching scheme, two cascaded detectors are used to detect noise, and two corresponding estimators are employed to effectively and efficiently filter the noise in an image. In the process of training, a method according to patch clustering is utilized, and genetic programming (GP) is subsequently applied to determine the optimum filter (wavelet-domain filter) for each individual cluster, while in testing part, the optimum filter trained beforehand by GP is recovered and used on the inputted corrupted patch. This adaptive structure is employed to cope with several noise types. Experimental and comparative analysis results show that the denoising performance of the proposed method is superior to that of existing denoising methods as per both quantitative and qualitative assessments.

Wavelet-domain TI Wiener-like filtering for complex MR data denoising

Magnetic resonance (MR) images are affected by random noises, which degrade many image processing and analysis tasks. It has been shown that the noise in magnitude MR images follows a Rician distribution. Unlike additive Gaussian noise, the noise is signal-dependent, and consequently difficult to reduce, especially in low signal-to-noise ratio (SNR) images. Wirestam et al. in [20] proposed a Wiener-like filtering technique in wavelet-domain to reduce noise before construction of the magnitude MR image. Based on Wirestam's study, we propose a wavelet-domain translation-invariant (TI) Wiener-like filtering algorithm for noise reduction in complex MR data. The proposed denoising algorithm shows the following improvements compared with Wirestam's method: (1) we introduce TI property into the Wiener-like filtering in wavelet-domain to suppress artifacts caused by translations of the signal; (2) we integrate one Stein's Unbiased Risk Estimator (SURE) thresholding with two Wiener-like filters to make the hard-thresholding scale adaptive; and (3) the first Wiener-like filtering is used to filter the original noisy image in which the noise obeys Gaussian distribution and it provides more reasonable results. The proposed algorithm is applied to denoise the real and imaginary parts of complex MR images. To evaluate our proposed algorithm, we conduct extensive denoising experiments using T1-weighted simulated MR images, diffusion-weighted (DW) phantom and in vivo data. We compare our algorithm with other popular denoising methods. The results demonstrate that our algorithm outperforms others in term of both efficiency and robustness.

EMGdi signal enhancement based on ICA decomposition and wavelet transform

Diaphragmatic electromyogram (EMGdi) signal plays an important role in the diagnosis and analysis of respiratory diseases. However, EMGdi recordings are often contaminated by electrocardiographic (ECG) interference, which posing serious obstacle to traditional denoising approaches due to overlapped spectra of these signals. In this paper, a novel method based on wavelet transform and independent component analysis (ICA) is proposed to remove the ECG interference from noisy EMGdi signals. With the proposed method, the original independent components of contaminated EMGdi signal were first obtained with ICA. Then the ECG components contained were removed by a specially designed wavelet domain filter. After that, the purified independent components were reconstructed back to the original signal space by ICA to obtain clean EMGdi signals. Experimental results achieved on practical clinical data show that the proposed approach is better than several traditional methods include wavelet transform (WT), ICA, digital filter and adaptive filter in ECG interference removing.

An adaptive edge-preserving image denoising technique using patch-based weighted-SVD filtering in wavelet domain

Image denoising has always been one of the standard problems in image processing and computer vision. It is always recommendable for a denoising method to preserve important image features, such as...

Code acquisition method based on wavelet transform filtering

In order to improve the acquisition probability of satellite navigation signals, this paper proposes a novel code acquisition method based on wavelet transform filtering. Firstly, the signal vector based on the signal passing through a set of partial matched filters(PMFs) is built. Then, wavelet domain filtering is performed on the signal vector value. Since the correlation signal is low in frequency and narrow in bandwidth, the noise out-of-band can be filtered out and the most of the useful signal energy is retained.Thus this process greatly improves the signal to noise ratio(SNR).Finally, the detection variable when the filtered signal goes through the combination process is constructed and the detection based on signal energy is made. Moreover, for the better retaining useful signal energy, the rule of selection of wavelet function has been made. Simulation results show the proposed method has a better detection performance than the normal code acquisition methods under the same false alarm probability.

Speckle suppression in SAR images employing modified anisotropic diffusion filtering in wavelet domain for environment monitoring

Synthetic Aperture Radar (SAR) is a tool of coherent imagery utilized for meteorological and astronomical purposes. But, these images are contaminated with speckle noise which degrades the image quality and automatic information extraction becomes difficult. This paper presents an improved filtering technique which combines the Wavelets and proposed Anisotropic Diffusion (AD) filter for despeckling SAR images. The speckled image is initially decomposed into sub-bands using 2D-Discrete Wavelet Transform (2D-DWT) followed by application of modified AD filter. The diffusion coefficient presented in this modified AD filter consists of a combination of gradient and Laplacian operators. The spatial variation of this diffusion coefficient occurs in such a way that it prefers forward diffusion to backward diffusion resulting in effective reconstruction of structural content and detection of weak edges. The filtered sub-bands are then reconstructed after soft thresholding. Based on the simulation results as well as the values of image quality metrics; filtered SAR images obtained by the proposed speckle suppression methodology can be claimed better in comparison to other recent works.