There are various de-noising algorithms and optimization methods for different signal and noise characteristics. However, the signals used in real application may have deviations from the model. For example: signal and/or noise may not be stationary or a proper model for them may not be available. MCG (magnetocardiography) is an example signal, where conventional de-noising methods are not givi...

In this paper, the problem of de-noising of an image contaminated with Additive White Gaussian Noise (AWGN) is studied. This subject is an open problem in signal processing for more than 50 years. Local methods suggested in recent years, have obtained better results than global methods. However by more intelligent training in such a way that first, important data is more effective for training,...

in this paper, an optimal algorithm is presented for de-noising of medical images. the presented algorithm is based on improved version of local pixels grouping and principal component analysis. in local pixels grouping algorithm, blocks matching based on l2 norm method is utilized, which leads to matching performance improvement. to evaluate the performance of our proposed algorithm, peak sign...

To overcome the shortcomings such as significantly de-noising effect and easily losing the details of the image characteristics of the existing image de-noising methods, an image de-noising algorithm based on the hybrid wavelet transform was proposed. The algorithm integrated the advantages of wavelet de-noising retaining image details features and Wiener filter obtaining the optimal solution, ...

De-noising and De-blurring is the technique that uses procedures to subsidize and remove the strange and useless content in the images De-noising and de-blurring is very beneficial for detecting the diseases like cancers, tumors in human body. But noise and Blur are the major factors that degrade the quality of images and makes difficult to diagnose. Reconstructing the images is the way to over...

Power system fault identification using information conveyed by the wavelet analysis of power system transients is proposed for detecting types of transmission line faults. In this paper, a comparative study of wavelet based de-noising signal based on wavelet thresholding is proposed. Discrete Wavelet Transform (DWT) analysis of the transient disturbance caused as a result of occurrence some of...

Dual tree complex wavelet transform(DTCWT) is a form of discrete wavelet transform, which generates complex coefficients by using a dual tree of wavelet filters to obtain their real and imaginary parts. The purposes of de-noising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal or image. This paper proposes a method for removing white Gaussian noi...

The main purpose of this paper was to introduce an efficient algorithm for fault identification in fruits images. First, input image was de-noised using the combination of Block Matching and 3D filtering (BM3D) and Principle Component Analysis (PCA) model. Afterward, in order to reduce the size of images and increase the execution speed, refined Discrete Cosine Transform (DCT) algorithm was uti...

Introduction: An efficient method of tomographic imaging in nuclear medicine is positron emission tomography (PET). Compared to SPECT, PET has the advantages of higher levels of sensitivity, spatial resolution and more accurate quantification. However, high noise levels in the image limit its diagnostic utility. Noise removal in nuclear medicine is traditionally based on Fourier decomposition o...

This paper presents an approach which addresses both de-noising and contrast enhancement. In a multiscale wavelet analysis framework, we take advantage of both soft thresholding and hard thresholding wavelet shrinkage techniques to reduce noise. In addition, we carry out nonlinear processing to enhance contrast within structures and along boundaries. Feature restoration and enhancement are acco...