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

Rain can cause performance degradation of outdoor computer vision tasks. Thus, the
exploration of rain removal from videos or a single image has drawn considerable attention …

Deep learning (DL) methods have achieved state-of-the-art performance in the task of single
image rain removal. Most of current DL architectures, however, are still lack of sufficient …

Along with the deraining performance improvement of deep networks, their structures and
learning become more and more complicated and diverse, making it difficult to analyze the …

Removing rain streaks from a single image has been drawing considerable attention as rain
streaks can severely degrade the image quality and affect the performance of existing …

Many video enhancement algorithms rely on optical flow to register frames in a video
sequence. Precise flow estimation is however intractable; and optical flow itself is often a …

Most deraining works focus on rain streaks removal but they cannot deal adequately with
heavy rain images. In heavy rain, streaks are strongly visible, dense rain accumulation or …

Single image rain removal is a typical inverse problem in computer vision. The deep
learning technique has been verified to be effective for this task and achieved state-of-the-art …

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 …

Existing enhancement methods are empirically expected to help the high-level end
computer vision task: however, that is observed to not always be the case in practice. We …