Images are susceptible to various kinds of noises, which corrupt the pictorial information
stored in the images. Image de-noising has become an integral part of the image processing …

This paper proposes a deep learning approach for accelerating magnetic resonance
imaging (MRI) using a large number of existing high quality MR images as the training …

We propose an Iterative Mean Filter (IMF) to eliminate the salt-and-pepper noise. IMF uses
the mean of gray values of noise-free pixels in a fixed-size window. Unlike other nonlinear …

The term “blind denoising” refers to the fact that the basis used for denoising is learned from
the noisy sample itself during denoising. Dictionary learning-and transform learning-based …

In this study, we propose a new method, ie Adaptive Riesz Mean Filter (ARmF), by
operationalizing pixel similarity for salt-and-pepper noise (SPN) removal. Afterwards, we …

Abstract Strict '0-1'block-diagonal structure has been widely used for learning structured
representation in face recognition problems. However, it is questionable and unreasonable …

Image denoising is a fundamental problem in image processing. The switching filtering is a
popular approach to reduce the impulse noise. It faces two challenges including the impulse …

Anomaly detection aims to identify unusual behavior or discriminate abnormal samples by
referring to the normal samples of data. Most exiting anomaly detection approaches train the …

A new impulsive noise removal filter, adaptive dynamically weighted median filter (ADWMF),
is proposed. A popular method for removing impulsive noise is a median filter whereas the …

With the development of data acquisition technology, high-dimensional data clustering is an
important yet challenging task in data mining. Despite advances achieved by current …