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...

We address the problem of online de-noising a stream of input points. We assume that the clean data is embedded in a linear subspace. We present two online algorithms for tracking subspaces and, as a consequence, de-noising. We also describe two regularization schemas which improve the resistance to noise. We analyze the algorithms in the loss bound model, and specify some of their properties. ...

We proposed a technique to detect the global addition of noise to a digital image. As an anti-forensics tool, noise addition is typically used to disguise the visual traces of image tampering or to remove the statistical artifacts left behind by other operations. As such, the blind detection of noise addition has become imperative as well as beneficial to authenticate the image content and reco...

The objective of the research presented in this paper is to shed light into the benefits of multi-dimensional wavelet-based methodology applied to NMR biomolecular data analysis. Specifically, the emphasis is on noise reduction for enhanced component identification in multi-dimensional mixture regression. The contributions of this research are multi-fold. First, the wavelet-based noise reductio...

To efficiently eliminate the noise generated by triaxial accelerometer when collecting pigs’ behavioural data, this paper adopted SNR and MSE as indexes to evaluate de-noising effect of acceleration signal under various combinations wavelet basis, decomposition layer, threshold rule function. Based on optimal parameter combinations, de-noised data were divided into a training dataset test condu...

The widely used Total Variation de-noising algorithm can preserve sharp edge, while removing noise. However, since fixed regularization parameter over entire image, small details and textures are often lost in the process. In this paper, we propose a modified Total Variation algorithm to better preserve smaller-scaled features. This is done by allowing an adaptive regularization parameter to co...

Unlike Gaussian noise, Rician noise filtering is more challenging, since this type of noise exists in Functional Magnetic Resonance Imaging (fMRI) data which makes the analysis of fMRI data very difficult for experimental and clinical purposes. To cope with the situation, normally (smoothing) de-noising is done before the analysis of the data using conventional methods like Gaussian filtering a...

Impulsive noise is one of the imposed defectives degrades the quality of images. Performance of many image processing applications directly depends on the quality of the input image. Hence, it is necessary to de-noise the degraded images without losing their valuable information such as edges. In this paper we propose a method to remove impulsive noise from color images without damaging the ima...

The process of removing noise from the original image is still a demanding problem for researchers. There have been several algorithms and each has its assumptions, merits, and demerits. The prime focus of this paper is related to the pre processing of an image before it can be used in applications. The pre processing is done by de-noising of images. In order to achieve these de-noising algorit...