Clinical MRI data is normally corrupted by random noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. For this reason, de-noising methods are often applied to increase the SNR and improve image quality. Most of these methods work on single channel images by correcting each grey level using an implicit model of the surrounding region, but with...

Soft thresholding has been a standard wavelet de-noising procedure in many signal and image processing applications. Theoretically, it is also almost optimal in the sense of nearly achieving the minimax mean-squared error. Inspired by this property, this paper proposes the addition of coefficient de-noising before soft thresholding. This extra step serves to reduce noise in the empirical wavele...

A new de-noising and compression method for ECG signals has been developed based on the wavelet transform. It has been designed for mobile telecardiology scenarios, where reliability as well as spectral efficiency are essential. The signal is segmented into beats and a beat template is subtracted to them. Beat templates as well as residual signals are coded with a wavelet expansion. De-noising ...

Using wavelet transform (WT) for increasing signal-to-noise ratio (SNR) of discrete-time signals corrupted by additive noise is explained and compared with some other techniques (averaging, frequency filtration, correlation). Signal processing for de-noising is applied to basic periodical signals and repeated transients (in nondestructive ultrasonic testing of welds, where presence of flaws sho...

In order to preserve the integrity of edge and detail information in the underwater image, a NSCT de-noising method based on Non-local means with modified parameter is proposed. Since NSCT has the feature of translation invariance, it is used to decompose the underwater image in multi-scale and multi-direction. For the noise and detail information are normally distributed in the high frequency ...

This paper introduces the Laplace algorithm for de-noising in the cepstrum domain with applications to speech recognition. Our method uses Gaussian mixture priors for clean speech and noise cepstra and assumes that speech and noise mix linearly in the spectrum domain. The Laplace algorithm involves two steps (a) computing the posterior mode of the observed noisy cepstra and (b) Gaussian approxi...

Recent research has shown that deep neural network is very powerful for object recognition task. However, training the deep neural network with more than two hidden layers is not easy even now because of regularization problem. To overcome such a regularization problem, some techniques like dropout and de-noising were developed. The philosophy behind de-noising is to extract more robust feature...

The Phase-Locked Loop is used to track an incoming signal and provide accurate carrier phase measurements on GPS receivers. However, the PLL performance is affected by the thermal noise and dynamic stress. In order to resolve the conflict between reducing PLL noise and overcoming the dynamic stress, some compromises must be taken in PLL design. This paper proposes a wavelet packet de-noising te...

An image is considered as a collection of information stored as intensities and the occurrence of noises. The occurrence of noise present in the image causes degradation in the quality of the image. The basic idea behind image processing is how we estimate the correct pixel values. Image De-noising is one of the fundamental problems which is faced in image processing and computer vision .There ...

Color image normally contain of three main colors at the each pixel, but the digital cameras capture only one color at each pixel using color filter array (CFA). While through capturing in color image, some noise/artifacts is added. So, the both demosaicing and de-noising are the first essential task in digital camera. Here, both the technique can be solve sequentially and independently. A conv...