NDLI: CGGAN: a context-guided generative adversarial network for single image dehazing

Content Provider	IET Digital Library
Author	Zhou, Zhaorun Shi, Zhenghao
Abstract	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.
Starting Page	3982
Ending Page	3988
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 15, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.1153
Journal	IET Image Processing
Publisher Date	2020-12-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CGGAN Computer Vision Computer Vision And Image Processing Technique Content-extraction Net Context Extraction Decoder Context-extraction Net Context-guided Generative Adversarial Network Decoding Decoding Process Deep Feature Information Deep Features Different Haze Scenarios Extra Coarse Decoder Extracting Haze Feature Feature Extraction Feature-extraction Net Acts Filtering Method in Signal Processing Final Haze-free Image Fusion Decoder Fusion Net Generating Coarse Dehazing Image Image And Video Coding Image Coding Image Colour Analysis Image Denoising Image Enhancement Image Fusion Image Haze Removal Image Recognition Image Restoration Multiscale Information Neural Nets Original Encoder–decoder Single Image Dehazing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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