A Light Dual-Task Neural Network for Haze Removal

  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},
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
SDTCN: Similarity Driven Transmission Computing Network for Image Dehazing
A novel light-weight similarity driven transmission computing network called SDTCN that is guided by the attributes of transmission similarity is proposed that outperforms the state-of-the-art methods on synthetic and real-world images. Expand
Deep Dehazing Network With Latent Ensembling Architecture and Adversarial Learning
This paper uses the adversarial game between a pair of neural networks to accomplish end-to-end photo-realistic dehazing and proposes a task-driven training strategy that can optimize the object detection performance on dehazed images without updating the parameters of object detector. Expand
A survey on analysis and implementation of state-of-the-art haze removal techniques
The current state-of-the-art methods for haze free images, mainly from the last decade, are thoroughly examined in this survey, which systematically summarizes the hardware implementations of various haze removal methods in real time. Expand
A dual-task dual-domain model for blind MRI reconstruction
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  • Medicine, Computer Science
  • Comput. Medical Imaging Graph.
  • 2021
The blind reconstruction model demonstrates the best reconstruction ability in both 4x acceleration and 8x acceleration, and is rethink the MRI reconstruction model as a k-space inpainting task. Expand
Fusion of Mathematical Morphology with Adaptive Gamma Correction for Dehazing and Visibility Enhancement of Images
The proposed dehazing approach combining dark channel prior (DCP) with mathematical morphology and a visibility enhancement algorithm is able to eliminate halo artifacts in the restored images. Expand
Person Re-Identification Based on Graph Relation Learning
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  • Computer Science
  • Neural Process. Lett.
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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
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
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
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
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
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
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
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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