CGGAN: A Context Guided Generative Adversarial Network For Single Image Dehazing

by   Zhaorun Zhou, et al.

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.


page 2

page 8

page 9

page 10


Res2NetFuse: A Fusion Method for Infrared and Visible Images

This paper presents a novel Res2Net-based fusion framework for infrared ...

Guided Deep Decoder: Unsupervised Image Pair Fusion

The fusion of input and guidance images that have a tradeoff in their in...

Pyramid Embedded Generative Adversarial Network for Automated Font Generation

In this paper, we investigate the Chinese font synthesis problem and pro...

Gated Fusion Network for Single Image Dehazing

In this paper, we propose an efficient algorithm to directly restore a c...

T-Net: A Template-Supervised Network for Task-specific Feature Extraction in Biomedical Image Analysis

Existing deep learning methods depend on an encoder-decoder structure to...

Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI

Purpose: To introduce a dual-domain reconstruction network with V-Net an...

SiENet: Siamese Expansion Network for Image Extrapolation

Different from image inpainting, image outpainting has relative less con...

Please sign up or login with your details

Forgot password? Click here to reset