TY - GEN
T1 - Gated context aggregation network for image dehazing and deraining
AU - Chen, Dongdong
AU - He, Mingming
AU - Fan, Qingnan
AU - Liao, Jing
AU - Zhang, Liheng
AU - Hou, Dongdong
AU - Yuan, Lu
AU - Hua, Gang
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2019/1
Y1 - 2019/1
N2 - Image dehazing aims to recover the uncorrupted content from a hazy image. Instead of leveraging traditional low-level or handcrafted image priors as the restoration constraints, e.g., dark channels and increased contrast, we propose an end-to-end gated context aggregation network to directly restore the final haze-free image. In this network, we adopt the latest smoothed dilation technique to help remove the gridding artifacts caused by the widely-used dilated convolution with negligible extra parameters, and leverage a gated sub-network to fuse the features from different levels. Extensive experiments demonstrate that our method can surpass previous state-of-the-art methods by a large margin both quantitatively and qualitatively. In addition, to demonstrate the generality of the proposed method, we further apply it to the image deraining task, which also achieves the state-of-the-art performance.
AB - Image dehazing aims to recover the uncorrupted content from a hazy image. Instead of leveraging traditional low-level or handcrafted image priors as the restoration constraints, e.g., dark channels and increased contrast, we propose an end-to-end gated context aggregation network to directly restore the final haze-free image. In this network, we adopt the latest smoothed dilation technique to help remove the gridding artifacts caused by the widely-used dilated convolution with negligible extra parameters, and leverage a gated sub-network to fuse the features from different levels. Extensive experiments demonstrate that our method can surpass previous state-of-the-art methods by a large margin both quantitatively and qualitatively. In addition, to demonstrate the generality of the proposed method, we further apply it to the image deraining task, which also achieves the state-of-the-art performance.
UR - https://www.scopus.com/pages/publications/85063576199
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85063576199&origin=recordpage
U2 - 10.1109/WACV.2019.00151
DO - 10.1109/WACV.2019.00151
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings - IEEE Winter Conference on Applications of Computer Vision, WACV
SP - 1375
EP - 1383
BT - Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
PB - IEEE
T2 - 19th IEEE Winter Conference on Applications of Computer Vision (WACV 2019)
Y2 - 7 January 2019 through 11 January 2019
ER -