Open AccessCGGAN: a context‐guided generative adversarial network for single image dehazing
Author(s) -
Zhou Zhaorun,
Shi Zhenghao
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.1153
Subject(s) - computer science , artificial intelligence , encoder , decoding methods , context (archaeology) , feature extraction , pyramid (geometry) , computer vision , feature (linguistics) , pattern recognition (psychology) , algorithm , mathematics , paleontology , linguistics , philosophy , geometry , biology , operating system
Image haze removal is highly desired for the application of computer vision. This study proposes a novel context‐guided generative adversarial network (CGGAN) for single image dehazing. Of which, a novel new encoder–decoder is employed as the generator. In addition, it consists of a feature‐extraction net, a context‐extraction net, and a fusion net in sequence. The feature‐extraction net acts as an encoder, and is used for extracting haze features. The content‐extraction net is a multi‐scale parallel pyramid decoder and is used for extracting the deep features of the encoder and generating coarse dehazing image. The fusion net is a decoder and is used for obtaining the final haze‐free image. In order to get better dehazing results, multi‐scale information obtained during the decoding process of the context extraction decoder is used for guiding the fusion decoder. By introducing an extra coarse decoder to the original encoder–decoder, the CGGAN can make better use of the deep feature information extracted by the encoder. To ensure that the proposed CGGAN works effectively for different haze scenarios, different loss functions are employed for the two decoders. Experiments results show the advantage and the effectiveness of the proposed CGGAN, evidential improvements over existing state‐of‐the‐art methods are obtained.