  Image haze removal is highly desired for the application of computer vision.
This paper proposes a novel Context Guided Generative Adversarial Network
(CGGAN) for single image dehazing. Of which, an novel new encoder-decoder is
employed as the generator. And it consists of a feature-extraction-net, a
context-extractionnet, and a fusion-net in sequence. The feature extraction-net
acts as a encoder, and is used for extracting haze features. The
context-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. To obtain more better 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 our CGGAN work effectively for
different haze scenarios, different loss functions are employed for the two
decoders. Experiments results show the advantage and the effectiveness of our
proposed CGGAN, evidential improvements over existing state-of-the-art methods
are obtained.
