Grand Challenge

Realistic Single Image Recovering in Adverse Weather

Recent News


Outdoor scenes are often affected by fog, haze, rain, and smog. Poor visibility in the atmosphere is due to suspended particles.

This challenge is meant to consolidate research efforts about single image recovering in adverse weather, especially hazy and rainy days. The challenge consists of two tracks: Hazy Image Recovering (HIR) and Rainy Image Recovering (RIR). In both tracks the researchers are required to recover sharp images from give degraded (hazy and rainy) inputs.


The dataset consists of two parts: rainy dataset and hazy dataset. Both these two datasets including training, validation, and test data.

Hazy dataset: We provide 4, 675 real-world hazy images collected from traffic surveillance scene, all of which are labeled with object bounding boxes and categories (car, bus, bicycle, motorcycle, and pedestrian), for validation and testing purposes.

Rainy dataset: We provide 4,000 real-world rainy images captured at various scenes, including rain streak, raindrop, and rain and mist images, all of which are labeled with object bounding boxes and categories (bike, car, human, truck, motorbike etc.)

Important Dates

  • May 27, Test image submission deadline.
  • May 27, Challenge paper submission due.
  • Jul. 1 , Evaluation results announcement:.
  • Jul. 1 , Notification of paper acceptance.
  • Jul. 15, Deadline of camera-ready paper.


Dr. Jiaying Liu
Associate Professor, Institute of Computer Science and Technology
Peking University, Beijing, P.R. China
Dr. Wenqi Ren
Assistant Professor, Institute of Information Engineering
Chinese Academy of Sciences, Beijing, P.R. China
Dr. Zhangyang Wang
Assistant Professor, Department of Computer Science & Engineering
Texas A&M University, US