Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …

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

Background Digital images are captured using sensors during the data acquisition phase,
where they are often contaminated by noise (an undesired random signal). Such noise can …

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 …

Depth image denoising is increasingly becoming the hot research topic nowadays, because
it reflects the three-dimensional scene and can be applied in various fields of computer …

We propose a new deep network architecture for removing rain streaks from individual
images based on the deep convolutional neural network (CNN). Inspired by the deep …

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 …

Rain is a common weather phenomenon, where object visibility varies with depth from the
camera and objects faraway are visually blocked more by fog than by rain streaks. Existing …

In this paper, we address a rain removal problem from a single image, even in the presence
of heavy rain and rain streak accumulation. Our core ideas lie in our new rain image model …

Rain streaks, particularly in heavy rain, not only degrade visibility but also make many
computer vision algorithms fail to function properly. In this paper, we address this visibility …