CGGAN: A Context Guided Generative Adversarial Network For Single Image Dehazing
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.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderGenerative Adversarial NetworkImage DehazingSingle Image DehazingSimilar Papers 제목 키워드 기반
BCGGAN: Ballistocardiogram artifact removal in simultaneous EEG-fMRI using generative adversarial network
Due to its advantages of high temporal and spatial resolution, the technology of simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) acquisition and analysis has attracted much attention, a…
EEGElectroencephalogram (EEG)Generative Adversarial Network3D Facial Geometry Recovery from a Depth View with Attention Guided Generative Adversarial Network
We present to recover the complete 3D facial geometry from a single depth view by proposing an Attention Guided Generative Adversarial Networks (AGGAN). In contrast to existing work which normally requires two or more de…
Generative Adversarial NetworkMy Body My Choice: Human-Centric Full-Body Anonymization
In an era of increasing privacy concerns for our online presence, we propose that the decision to appear in a piece of content should only belong to the owner of the body. Although some automatic approaches for full-body…
Full-body anonymizationCLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks
Transferable targeted adversarial attacks aim to mislead models into outputting adversary-specified predictions in black-box scenarios. Recent studies have introduced \textit{single-target} generative attacks that train …
EatGAN: An Edge-Attention Guided Generative Adversarial Network for Single Image Super-Resolution
Single-image super-resolution (SISR) is an important task in image processing, aiming to enhance the resolution of imaging systems. Recently, SISR has made a significant leap and achieved promising results with deep lear…
Image Super-Resolution