Received Jul 8, 2017 Revised Nov 20, 2017 Accepted Dec 11, 2017 This paper presents a novel audio de-noising scheme in a given speech signal. The recovery of original from the communication channel without any noise is a difficult task. Many de-noising techniques have been proposed for the removal of noises from a digital signal. In this paper, an audio denoising technique based on Short Time F...

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...

Analysis and interpretation of 2D gel images is a very important task in proteonomics study. The nonlinearties in the gel formation and image acquisition process leads to distortions, overlapped protein spots, saturated spots, faint spots, nonlinear intensity and uneven background in the gel images. As a result, pre-processing steps are important before image-analysis. De-noising is one of the ...

The images usually bring different kinds of noise in the process of receiving, coding and transmission. In this paper the Curvelet transform is used for de-noising of image. Two digital implementations of the Curvelet transform (a multiscale transform) viz the Unequally Spaced Fast Fourier Transform (USFFT) and the Wrapping Algorithm are used to de-noise images degraded by different types of no...

We present a novel signal de-noising algorithm for recovery of signals corrupted by a high levels of noise and applicable in the situations of low sampling rates. This method uses a modi cation of kernel partial least squares regression (KPLS) de ned in reproducing kernel Hilbert space [1]. We treat signal de-noising as a regression problem, in which de-noising consists of estimation of functio...

Pulsed terahertz (T-ray) imaging systems represent an extremely promising method of obtaining sub-millimetre spectroscopic measurements for a wide range of applications. This paper investigates a number of techniques for optimally processing terahertz data. Speci cally we consider wavelet de-noising andWiener deconvolution algorithms. A goal of this research is the design and implementation of ...

The paper puts forward an image de-noising method based on 2D wavelet transform with the application of the method in agricultural data collection system. As the there are influences of various factors in the collection process through wireless image sensor network, the detail signals of each scale are obtained from multi-scale analysis to replace the original signals with smooth low-frequency ...

Abstract: EEG signals are the versatile tool for detection of various kinds of Brain activities and diseases. But when the EEG data has been recorded for analysis purpose it is contaminated by different noise signals which are caused due to power line interference, electrode movement, base line wander, muscle movement (EMG) etc. and these days the E-health care system introduces in which there ...

We present a kernel based approach for image de-noising in the spatial domain. The crux of evaluation for the kernel weights is addressed by a Bayesian regression. This approach introduces an adaptive filter, well preserving edges and thin structures in the image. The hyper-parameters in the model as well as the predictive distribution functions are estimated through an efficient iterative sche...