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,...

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, ...

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...

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...

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...

˗Image de-noising is an important challenging issue in image pre-processing. Two popular methods to the problem are partial differential equation (PDE) based nonlinear diffusion method and singular value decomposition (SVD) method. Various image de-noising algorithms based on these two methods have been independently developed. This paper proposes an approach for image de-noising by performing ...

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 bett...

De-noising is one of the most important applications of image processing which has been applied to a wide variety of real world problems. De-noising allows for improving image quality in imaging modalities that are noise prone. A lot of research work has gone in to improving quality of 2D images using various de-noising techniques but new modalities of imaging such as industrial 3D X-ray comput...

The amplitude of nuclear magnetic resonance (NMR) longing signal general is very small while the signal to noise ratio (SNR) is also very low, so de-noising NMR signal before T2 spectrum inversion is necessary and important. In this paper, an improved de-noising algorithm based on wavelet transform is put forward. The main idea of this improved algorithm is that NMR signal is divided to several...

Local adaptive image de-noising in transform domain is a powerfull tool for adapting to unknown smoothness of the images. In this work we propose to perform local adaptive denoising with adaptively varying local transform support size rather than using a transform with ¿xed size. We use a special rule (Intersection of Con¿dence Intervals ICI) to select the optimum window sizes locally. The algo...