In order to obtain favorable communication performance, impulsive noise needs to be
suppressed which is a major interference in scenarios such as broadcasting system and …

Deep learning has been widely applied in image processing and computer vision due to its
powerful learning capability. Although some learning models have been proposed to …

The removal of impulse noise is a crucial pre-processing step in image processing systems.
In recent years, numerous noise-removal methods have been proposed to improve …

Denoising convolutional neural network (DnCNN) has achieved competitive denoising
performance for Gaussian noise using residual learning. The same idea can also be applied …

We introduce a new method of noise suppression using fully convolutional neural networks
for salt and pepper noise. We adopt a well-known residual learning framework to get …

Denoising convolutional neural networks (DnCNNs), initially developed for Gaussian noise
removal, are powerful nonlinear mapping models in image processing. After changes in …

Impulsive noise is a common impediment in many wireless, power line communication
(PLC), and smart grid communication systems that prevents the system from achieving error …

Image pre-processing is an important operation that is used to redefine an image to improve
human visual perception and information extraction. To de-noise an image tainted with …

In Measurement While Drilling (MWD), the mud pulse signal is often used to transmit
information, but the strong noise characteristic of the mud signal makes the signal …

The qualitative performances of the digital image processing methods are degraded due to
the presence of impulse noise. The conventional median filter and its advanced versions …