In recent years, the data-driven based FDD (Fault Detection and Diagnosis) of high-speed
train electric traction systems has made rapid progress, as the safe operation of traction …

In this paper, we propose an advanced framework for image denoising using bilateral
filtering based non-subsampled shearlet transform (NSST). Initially, we apply the NSST to …

In recent years, background subtraction techniques have been used in vision and image
applications for moving target detection. However, most methods cannot provide fine results …

Preservation of geometric components during image denoising using weighted bilateral filter
and curvelet transforms is explored in this research. The proposed method emphases the …

The image often contains noises due to several factors such as a problem in devices or due
to an environmental problem etc. Noise is mainly undesired information, which degrades the …

In the past decade, several image denoising techniques have been developed aiming at
recovering signals from noisy data as much as possible along with preserving the features of …

Denoising an image, while retaining the important features of the image, has been a
fundamental problem in image processing. Dual-tree complex wavelet transform is a …

In this study, we employ wavelet analysis, which involves low-frequency reconstruction, as
well as a hard and a soft threshold function to suppress static effects. Our research has …

Low-rank regularization approximated by a nuclear norm has been proven its ability in
image denoising. However, the nuclear norm is just a suboptimization of the rank norm …

Abstract Error-based Orthogonal Matching Pursuit employed in many image denoising
algorithms (eg, K-means singular value decomposition (K-SVD) algorithm) tries to …