Deep convolutional neural networks (CNNs) are used for image denoising via automatically
mining accurate structure information. However, most of existing CNNs depend on enlarging …

Due to strong learning ability, convolutional neural networks (CNNs) have been developed
in image denoising. However, convolutional operations may change original distributions of …

Hazy images captured under ill-posed scenarios with scattering medium (ie haze, fog, or
smoke) are contaminated in visibility. Inevitably, these images are further degraded by …

In this article, we address the problem of waveform design for bistatic multiple-input multiple-
output dual-function radar-communication system with focuses on both the multi-user …

For flexible non-blind image denoising, existing deep networks usually concatenate noisy
image and noise level map as the input for handling various noise levels with a single …

Limited by storage conditions, the degradation of old photos exhibits complex and diverse
features. Existing image restoration methods heavily rely on features extracted from a single …

In the past decade, guided image filtering (GIF) has emerged as a successful edge-
preserving smoothing technique designed to remove noise while retaining important edges …

CT scans, which are another name for computed tomography, are one of the many
diagnostic tools where the use of photon-counting allows for the differentiation of materials …

In this paper, we propose the hybrid locally kernel-based weighted least square (HKLS)
method to reduce the noise of the range images captured from the Microsoft Kinect sensor …

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 …