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Identification of dominant periodicities is a very important but difficult task in astronomical time series analysis. In the present paper, a new method of periodic identification is proposed in which empirical mode decomposition (EMD) and wavelet transform analysis (WTA) are used in combination. We firstly apply EMD method to decompose a time series into several intrinsic mode functions (IMFs), and...
Automated isotope identification has long been an important problem in homeland security and nuclear emergency response. This process is difficult for low-resolution spectra because peaks can be significantly overlapping. Also, their areas can be difficult to determine because of the fluctuating baseline due to the Compton continuum across the whole spectrum. The wavelet transform stands out among...
The measurements obtained from the acquiring PET system tend to be very noisy, since randoms and scatter contamination events as well as detector efficiency are strong sources of noise. In particular, for the small animal reconstructed images, this problem becomes severe corrupting areas of interest between organs, making the identification process even more difficult. For that reason, a regularization...
Most of the current pitch detection algorithms can not work well under the high noise environment. For this reason, a pitch detection algorithm for noisy speech signals based on wavelet transform and autocorrelation function is proposed. First, the noisy speech signals are decomposed by three-layer wavelet transform in order to get rid of the high frequency noise and obtain the approximate signals...
In most situations, acquired digital images always are corrupted by the wrong camera focus, serious illumination even missing data. This algorithm is presented for fusion of corrupted multi-sensor images by noise. Compared to the susceptible properties of PCA by large errors, the proposed method includes adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation...
A fresh image denoising algorithm using neighboring coefficients of nonsubsampled contourlet transform (NSCT) is presented. NSCT can obtain better results compared to traditional wavelets and contourlet transform due to its outstanding properties. But denoising by NSCT using classical threshold doesn't take account of coefficients correlation. Noise suppressing method presented calculates the hard...
The purpose of this paper is to study a new method of de-noising images corrupted with additive white Gaussian noise. Based on the Chen-Mobius transform, the idea of modulation and demodulation in Chen-Mobius communication system is applied in the image de-noising. The evaluation results of the Chen-Mobius transform of some often-used waveforms are applied in the anti-noise of image. For image de-noising,...
In this paper, a perceptual audio compressor is developed with the use of the Wavelet Transform in an Embedded System. Former compressors were not able to achieve remarkable compression ratios while maintaining the same format type (in this case .wav). The present project makes an efficient use of a Transform which enables appropriate time-frequency tracking, without perceptual losses. As it is known,...
Segmentation of moving objects in a video sequence is a primary mission of many computer vision tasks. However, shadows extracted along with the objects can result in large errors in object localization and recognition. We propose a novel method of moving shadow detection using wavelets and watershed segmentation algorithm, which can effectively separate the cast shadow of moving objects in a scene...
This paper proposed a novel method to enhance mammogram images used in detecting early signs of breast cancer. The proposed method uses a three level pyramid decomposition scheme that applies the squeeze box filter (SBF) instead of low-pass filtering. A previously proposed non-linear local enhancement technique is applied to the difference images to contrast enhance the structural details of a mammogram...
Wavelet-based demosaicing techniques have the advantage of being computationally relatively fast, while having a reconstruction performance that is similar to state-of-the-art techniques. Because the demosaicing rules are linear, it is fairly simple to integrate denoising into the demosaicing. In this paper, we present a method that performs joint denoising and demosaicing, using a Gaussian Scale...
Image denoising methods have been implemented in both spatial and transform domains. Each domain has its advantages and shortcomings, which can be complemented by each other. State-of-the-art methods like block-matching 3D filtering (BM3D) therefore combine both domains. However, implementation of such methods is not trivial. We offer a hybrid method that is surprisingly easy to implement and yet...
Blurred image restoration is a longstanding and critical research problem. We addressed this problem using Expectation Maximization (EM) based approach in wavelet domain. The sparsity property of wavelet coefficients is modeled using the class of Gaussian Scale Mixture (GSM), which represents the heavy-tailed statistical distribution, suitable for natural images. The underlying original image and...
Image compression has emerged as a major research area due to the phenomenal growth of applications that generate process and transmit images. Image compression can be sequential or progressive. Natural images contain edges, geometry, texture and other discontinuities (details that are oriented in various directions). In this paper, a comparative analysis of four images compression techniques (JPEG,...
An doubly adaptive nonlocal-means (DANLM) algorithm for image denoising is proposed in this paper. The wavelet-Based denoising method is first used to get the pilot image, and then the pilot image is divided into texture and smooth regions base on mathematical morphology. Finally, the noisy image is denoised by nonlocal means method with patch window and search window adaptively adjust to the local...
The super-resolution approach has attracted substantial attention in the field of image processing in view of its capability of providing higher resolution from low resolution image sequences. Interesting techniques have been developed and practical results have been obtained. However, in several theoretical investigations, good results are often corroborated by simulations, which limits the use of...
The THz image has lower contrast and bigger noise because the THz radiant power is small, so a multi-scales nonlinear enhancement method of THz image is proposed for the purpose of improving the image definition. The THz image is decomposed into multi-scales detail coefficients and approximation coefficients by utilizing the wavelet transform. The detail coefficients are taken to denoise and histogram...
Image de-noising algorithm is the crux of digital X-ray inspection for inner defects in electronically package production. Due to various factors the actual noise derived from the real X-ray inspection images in site have a certain space-time correlation or peculiar characteristics of colored noise, specifically, the frequency distribution of the noise in the frequency domain by Fast Fourier Transform(FFT)...
It is well known that a signal of atomic clock is effected by various noises. In order to obtain the high precision time scale, it is important and necessary to filter or weaken the noises from the atomic clock's signal. The smooth processing of the atomic clock signal may be realized by Hilbert-Huang transform. With empirical mode decomposition method, a clock's signal is decomposed into single component...
Marine radar image often has a lot of noise signal, the noise ratio of the radar image is very low. So a method based on a combination of wavelet transform, K-distributed sea clutter threshold segmentation and most of the interpolation repair method is presented in this paper, which can deal with the same frequency noise of marine radar image. This paper comes up with use wavelet transform to determine...
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