A Modified Syn2Real Network for Nighttime Rainy Image Restoration

EasyChair Preprint 4351

Abstract

The restoration or enhancement of rainy images at nighttime is of great significance to outdoor computer vision applications such as self-driving and traffic surveillance.While image deraining has drawn increasingly research attention currently and the majority of deraining methods are able to achieve satisfying performance for daytime image rain removal, there are few related studies for nighttime image deraining, as the conditions of nighttime rainy scenes are more complicated and challenging.To address the nighttime image deraining issues, we designed an improved model based on the Syn2Real network, called NIRR. In order to obtain good rain removal and visual effect under the nighttime rainy scene, we propose a new refined loss function for the supervised learning phase, which combines the perceptual loss and SSIM loss.The qualitative and quantitative experimental results show that our proposed method outperforms the state-of-the-arts whether it is on the synthetic nighttime rainy image or on the real-world nighttime rainy image.

Keyphrases: Gaussian processes, Nighttime image deraining, Semi-supervised network

@booklet{EasyChair:4351,
  author    = {Qunfang Tang and Jie Yang and Haibo Liu and Zhiqiang Guo},
  title     = {A Modified Syn2Real Network for Nighttime Rainy Image Restoration},
  howpublished = {EasyChair Preprint 4351},
  year      = {EasyChair, 2020}}