It can classify snow particles according to their sizes and conduct snow removal in different scales.
Recently, vehicle similarity learning, also called re-identification (ReID), has attracted significant attention in computer vision.
At the KC, the student network aims to learn the comprehensive bad weather removal problem from multiple well-trained teacher networks where each of them is specialized in a specific bad weather removal problem.
The rainy scenarios can be categorized into two classes: moderate rain and heavy rain scenes.
Moreover, due to the limitation of existing snow datasets, to simulate the snow scenarios comprehensively, we propose a large-scale dataset called Comprehensive Snow Dataset (CSD).
In addition, to further enhance the performance of the method for haze removal, a patch-map-based DCP has been embedded into the network, and this module has been trained with the atmospheric light generator, patch map selection module, and refined module simultaneously.
In this paper, a very effective method to solve the contiguous face occlusion recognition problem is proposed.
We first propose a novel nonconvex rank surrogate on the general rank minimization problem and apply this to the corrupted image completion problem.