When autonomous systems consider the performance of accuracy and transferability simultaneously, several AI methods, like adversarial learning, reinforcement learning (RL) and meta-learning, show their powerful performance.
Rain removal in images is an important task in computer vision filed and attracting attentions of more and more people.
Removing the rain streaks from single image is still a challenging task, since the shapes and direc-tions of rain streaks in the synthetic datasets are very different from real images.
The goal of single-image deraining is to restore the rain-free background scenes of an image degraded by rain streaks and rain accumulation.
However, in practice it is rather common to have no un-paired images in real deraining task, in such cases how to remove the rain streaks in an unsupervised way will be a very challenging task due to lack of constraints between images and hence suffering from low-quality recovery results.
In this paper, we propose a novel end-to-end Neuron Attention Stage-by-Stage Net (NASNet), which can solve all types of rain model tasks efficiently.
Different rain models and novel network structures have been proposed to remove rain streaks from single rainy images.
However, the existing methods usually do not have good generalization ability, which leads to the fact that almost all of existing methods have a satisfied performance on removing a specific type of rain streaks, but may have a relatively poor performance on other types of rain streaks.
Then, a subjective study is conducted on our DQA database, which collects the subject-rated scores of all de-rained images.