Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Super resolution (SR) is an eminent system in the field of computer vison and image
processing to improve the visual perception of the poor-quality images. The key objective of …

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

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem.
However, CNNs depend on deeper network architectures to improve performance of image …

Convolutional neural networks (CNNs) have obtained remarkable performance via deep
architectures. However, these CNNs often achieve poor robustness for image super …

Deep convolutional neural networks (CNNs) for image denoising have recently attracted
increasing research interest. However, plain networks cannot recover fine details for a …

Deep learning models are widely being employed for object detection due to their high
performance. However, the majority of applications that require object detection are …

Low-light image enhancement aims to recover normal-light images from the images
captured under very dim environments. Existing methods cannot well handle the noise, color …

Deep convolutional neural networks (CNNs) have been widely applied for low-level vision
over the past five years. According to the nature of different applications, designing …

In the field of remote sensing, due to memory consumption and computational burden, the
single-image super-resolution (SISR) methods based on deep convolution neural networks …