Image Denoising is an important part of diverse image processing and computer vision problems. The important property of a good image denoising model is that it should completely remove noise as far as possible as well as preserve edges. One of the most powerful and perspective approaches in this area is image denoising using discrete wavelet transform (DWT). In this paper comparative analysis ...

When the signal in the form of image is processed, it gets distorted and further processing does not provide good results. Hence it is very important to get back the image in its original noise free condition. In this paper we present an image denoising method for noise removal. In this work, a wavelet-based multiscale linear minimum mean square-error estimation (LMMSE) scheme for image denoisi...

Wavelet decompositions of Raman spectra were investigated with respect to their usability for spike removal and denoising of the raw data. It could be shown that those operations should be performed sequentially. Suppression of spikes is not straightforwardly possible by wavelet transformation; however, the wavelet transform may be used to recognize the spikes by their first level detail coeffi...

This paper used three theoretically different algorithms for reducing noise in event-related potential (ERP) data. It examined the possibility that a hybrid of these methods could show gains in noise reduction beyond that obtained with any single method. The well-known ERP oddball paradigm was used to evaluate three denoising methods: statistical wavelet transform (wavelet-Z), a smooth subspace...

The real world signals do not exist without noise. Wavelet Transform based denoising is a powerful method for suppressing noise in signals. In this paper, signal denoising based on Double-Density Discrete Wavelet Transform (DDDWT) and Dual-Tree Discrete Wavelet Transform (DTDWT) methods are implemented with optimum values of threshold point and level of decomposition. Based on the intensity of ...

Physiological functions (e.g., cerebral blood flow, glucose metabolism, and neuroreceptor binding) can be investigated as parameters estimated by kinetic modeling using dynamic positron emission tomography (PET) images. Imaging of these physiological parameters, called parametric imaging, can locate the regional distribution of functionalities. However, the most serious technical issue affectin...

The denoising of a natural image corrupted by Gaussian noise is a problem in signal or image processing Even though much work has been done in the field of wavelet thresholding, most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds. This paper describes a new method for suppression of noise in image by fusing the wavelet Denoising technique ...

In this paper, we link concepts from nonuniform sampling, smoothness function spaces, interpolation, and wavelet denoising to derive a new multiscale interpolation algorithm for piecewise smooth signals. We formulate the optimization of nding the signal that balances agreement with the given samples against a wavelet-domain regularization. For signals in the Besov space B p (Lp), p 1, the optim...

Recent interest in the West Indian manatee (Trichechus manatus latirostris) vocalizations has been primarily induced by an effort to reduce manatee mortality rates due to watercraft collisions. A warning system based on passive acoustic detection of manatee vocalizations is desired. The success and feasibility of such a system depends on effective denoising of the vocalizations in the presence ...