A Light Dual-Task Neural Network for Haze Removal

@article{Zhang2018ALD,
  title={A Light Dual-Task Neural Network for Haze Removal},
  author={Yu Zhang and Xinchao Wang and Xiaojun Bi and Dacheng Tao},
  journal={IEEE Signal Processing Letters},
  year={2018},
  volume={25},
  pages={1231-1235}
}
Single-image dehazing is a challenging problem due to its ill-posed nature. [...] Key Method We use transmission map estimation as an auxiliary task to assist the main task, haze removal, in feature extraction and to enhance the generalization of the network. In LDTNet, the haze-free image and the transmission map are produced simultaneously. As a result, the artificial prior is reduced to the smallest extent. Extensive experiments demonstrate that our algorithm achieves superior performance against the state…Expand
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References

SHOWING 1-10 OF 50 REFERENCES
DehazeNet: An End-to-End System for Single Image Haze Removal
TLDR
This paper proposes a trainable end-to-end system called DehazeNet, for medium transmission estimation, which takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. Expand
Single Image Dehazing via Multi-scale Convolutional Neural Networks
TLDR
A multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps by combining a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale network which refines results locally. Expand
An All-in-One Network for Dehazing and Beyond
TLDR
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net), designed based on a re-formulated atmospheric scattering model that directly generates the clean image through a light-weight CNN. Expand
Gated Fusion Network for Single Image Dehazing
TLDR
An efficient algorithm to directly restore a clear image from a hazy input using an end-to-end trainable neural network that consists of an encoder and a decoder is proposed. Expand
A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior
TLDR
A simple but powerful color attenuation prior for haze removal from a single input hazy image is proposed and outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect. Expand
Efficient Image Dehazing with Boundary Constraint and Contextual Regularization
TLDR
An efficient regularization method to remove hazes from a single input image and can restore a high-quality haze-free image with faithful colors and fine image details is proposed. Expand
Factorizing Scene Albedo and Depth from a Single Foggy Image
  • L. Kratz, K. Nishino
  • Computer Science
  • 2009 IEEE 12th International Conference on Computer Vision
  • 2009
TLDR
A novel probabilistic method is introduced that fully leverages natural statistics of both the albedo and depth of the scene to resolve this ambiguity and achieves more accurate restoration compared to state-of-the-art methods that focus on only recovering sceneAlbedo or depth individually. Expand
A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image
TLDR
A novel approach to restore a single image degraded by atmospheric phenomena such as fog or haze is introduced, based on an extensive study on a large data set of images, and validated based on a metric that measures the contrast but also the structural changes. Expand
FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification
TLDR
This paper proposes an end-to-end multi-context collaborative deep network for removing distortions from single fisheye images and shows that the proposed model significantly outperforms current state of the art methods. Expand
Single Image Haze Removal Using Dark Channel Prior
Haze brings troubles to many computer vision/graphics applications. It reduces the visibility of the scenes and lowers the reliability of outdoor surveillance systems; it reduces the clarity of theExpand
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