Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly
emerging efficient machine learning techniques which offer promising solutions for …

At the crossing of the statistical and functional analysis, there exists a relentless quest for an
efficient image denoising algorithm. In terms of greyscale imaging, a plethora of denoising …

This paper introduces a novel denoising approach making use of a deep convolutional
neural network to preserve image edges. The network is trained by using the edge map …

Used in a wide variety of transform based statistical image processing techniques, the
hidden Markov tree (HMT) model with Gaussian mixtures is typically employed to capture …

In digital image processing, noise suppression from the original signal is still considered as
biggest challenge till today. Image denoising refers to the process in which it evaluates the …

In order to improve the noise reduction performance and the clarity of denoising images, a
composite convolutional neural network composed of the convolutional autoencoder …

An image denoising method is proposed for ultrasonic logging images with severe noise.
The proposed method works on a variational Bayesian framework using block sparse prior …

Convolutional neural networks (CNNs) have made great achievements in the field of image
denoising but can still be improved. We introduce a network structure, namely, multifeature …

As an important multiscale geometric analysis tool, the nonsubsampled contourlet transform
(NSCT) has a strong ability in capturing anisotropy and directional features of images. This …

This work presents a new ultrasound image despeckling method based on the maximum
likelihood principle that effectively exploits non-local information for estimating noise-free …