Severe weather conditions, such as rain and snow, adversely affect the visual quality of
images captured under such conditions, thus rendering them useless for further usage and …

De-raining, which aims at rain-steak removal from images, is a practical task in computer
vision. However, it is difficult due to its ill-posed nature. In this letter, we propose a deep …

Existing deep learning based de-raining approaches have resorted to the convolutional
architectures. However, the intrinsic limitations of convolution, including local receptive fields …

Single image rain streaks removal has recently witnessed substantial progress due to the
development of deep convolutional neural networks. However, existing deep learning based …

Single image rain removal plays an important role in numerous multimedia applications.
Existing algorithms usually tackle the deraining problem by the way of signal removal, which …

Single image rain streak removal is an extremely challenging problem due to the presence
of non-uniform rain densities in images. We present a novel density-aware multi-stream …

Deep convolutional neural networks (CNNs) have shown their advantages in the single
image de-raining task. However, most existing CNNs-based methods utilize only local …

Most advances in single image de-raining meet a key challenge, which is removing rain
streaks with different scales and shapes while preserving image details. Existing single …

Single image de-raining is an extremely challenging problem since the rainy image may
contain rain streaks which may vary in size, direction and density. Previous approaches …

Single Image Deraining (SID) is a relatively new and still challenging topic in emerging
vision applications, and most of the recently emerged deraining methods use the supervised …