The need to accurately estimate the speed of road vehicles is becoming increasingly
important for at least two main reasons. First, the number of speed cameras installed …

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 …

Transformers-based methods have achieved significant performance in image deraining as
they can model the non-local information which is vital for high-quality image reconstruction …

Image restoration tasks demand a complex balance between spatial details and high-level
contextualized information while recovering images. In this paper, we propose a novel …

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

Learning single image deraining (SID) networks from an unpaired set of clean and rainy
images is practical and valuable as acquiring paired real-world data is almost infeasible …

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 …

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 …

Image dehazing aims to recover the uncorrupted content from a hazy image. Instead of
leveraging traditional low-level or handcrafted image priors as the restoration constraints …

Rain streaks can severely degrade the visibility, which causes many current computer vision
algorithms fail to work. So it is necessary to remove the rain from images. We propose a …