SimHaze: game engine simulated data for real-world dehazing
Zhengyang Lou Huan Xu Fangzhou Mu Yanli Liu Xiaoyu Zhang Liang Shang Jiang Li Bochen Guan Yin Li Yu Hen Hu
Deep models have demonstrated recent success in single-image dehazing. Most
prior methods consider fully supervised training and learn from paired clean
and hazy images, where a hazy image is synthesized based on a clean image and
its estimated depth map. This paradigm, however, can produce low-quality hazy
images due to inaccurate depth estimation, resulting in poor generalization of
the trained models. In this paper, we explore an alternative approach for
generating paired clean-hazy images by leveraging computer graphics. Using a
modern game engine, our approach renders crisp clean images and their precise
depth maps, based on which high-quality hazy images can be synthesized for
training dehazing models. To this end, we present SimHaze: a new synthetic haze
dataset. More importantly, we show that training with SimHaze alone allows the
latest dehazing models to achieve significantly better performance in
comparison to previous dehazing datasets. Our dataset and code will be made
publicly available.