Several contemporaneous image processing and computer vision systems rely upon the full-
reference image quality assessment (IQA) measures. The single-scale structural similarity …

Vision-based aerial tracking has proven enormous potential in the field of remote sensing
recently. However, challenges such as occlusion, fast motion, and illumination variation …

This paper presents a fractional one-dimensional convolutional neural network (CNN)
autoencoder for denoising the Electroencephalogram (EEG) signals which often get …

Object feature pollution is one of the burning issues in vision-based UAV tracking, commonly
caused by occlusion, fast motion, and illumination variation. Due to the contaminated …

Limited by hardware conditions, imaging devices, transmission efficiency, and other factors,
high-resolution (HR) images cannot be obtained directly in clinical settings. It is expected to …

Recently, unmanned aerial vehicle (UAV) object tracking tasks have significantly improved
with the emergence of deep learning. However, owing to the object feature pollution caused …

A fractional-based compressed auto-encoder architecture has been introduced to solve the
problem of denoising electroencephalogram (EEG) signals. The architecture makes use of …

Blind Gaussian denoising is the process of removing noise from an image affected by
additive white Gaussian noise (AWGN) of unknown variance. This paper proposes a multi …

Soft computing (SC) includes computational techniques that are tolerant of approximations,
missing information, and uncertainty, and aim at providing effective and efficient solutions to …

Soft computing (SC) includes computational techniques that are tolerant of approximations,
missing information, and uncertainty, and aim at providing effective and efficient solutions to …