Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …

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 …

In this paper, an ill-posed problem of multiple adverse weather removal is investigated. Our
goal is to train a model with a'unified'architecture and only one set of pretrained weights that …

In this paper, we propose a Multi-Scale Boosted Dehazing Network with Dense Feature
Fusion based on the U-Net architecture. The proposed method is designed based on two …

Recent state-of-the-art super-resolution methods have achieved impressive performance on
ideal datasets regardless of blur and noise. However, these methods always fail in real …

Most video super-resolution methods super-resolve a single reference frame with the help of
neighboring frames in a temporal sliding window. They are less efficient compared to the …

The current superresolution (SR) methods based on deep learning have shown remarkable
comparative advantages but remain unsatisfactory in recovering the high-frequency edge …

Recently, deep convolutional neural networks (CNNs) have been successfully applied to the
single-image super-resolution (SISR) task with great improvement in terms of both peak …

Many methods have set state-of-the-art performance on restoring images degraded by bad
weather such as rain, haze, fog, and snow, however they are designed specifically to handle …

The goal of single-image deraining is to restore the rain-free background scenes of an
image degraded by rain streaks and rain accumulation. The early single-image deraining …