DTDN: Dual-task De-raining Network
- URL: http://arxiv.org/abs/2008.09326v1
- Date: Fri, 21 Aug 2020 06:32:42 GMT
- Title: DTDN: Dual-task De-raining Network
- Authors: Zheng Wang, Jianwu Li and Ge Song
- Abstract summary: We propose an end-to-end network, called dual-task de-raining network (DTDN)
It consists of two sub-networks: generative adversarial network (GAN) and convolutional neural network (CNN)
We show that our method outperforms several recent state-of-the-art methods.
- Score: 21.91416699094407
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Removing rain streaks from rainy images is necessary for many tasks in
computer vision, such as object detection and recognition. It needs to address
two mutually exclusive objectives: removing rain streaks and reserving
realistic details. Balancing them is critical for de-raining methods. We
propose an end-to-end network, called dual-task de-raining network (DTDN),
consisting of two sub-networks: generative adversarial network (GAN) and
convolutional neural network (CNN), to remove rain streaks via coordinating the
two mutually exclusive objectives self-adaptively. DTDN-GAN is mainly used to
remove structural rain streaks, and DTDN-CNN is designed to recover details in
original images. We also design a training algorithm to train these two
sub-networks of DTDN alternatively, which share same weights but use different
training sets. We further enrich two existing datasets to approximate the
distribution of real rain streaks. Experimental results show that our method
outperforms several recent state-of-the-art methods, based on both benchmark
testing datasets and real rainy images.
Related papers
- Semi-DRDNet Semi-supervised Detail-recovery Image Deraining Network via
Unpaired Contrastive Learning [59.22620253308322]
We propose a semi-supervised detail-recovery image deraining network (termed as Semi-DRDNet)
As a semi-supervised learning paradigm, Semi-DRDNet operates smoothly on both synthetic and real-world rainy data in terms of deraining robustness and detail accuracy.
arXiv Detail & Related papers (2022-04-06T12:35:27Z) - Online-updated High-order Collaborative Networks for Single Image
Deraining [51.22694467126883]
Single image deraining is an important task for some downstream artificial intelligence applications such as video surveillance and self-driving systems.
We propose a high-order collaborative network with multi-scale compact constraints and a bidirectional scale-content similarity mining module.
Our proposed method performs favorably against eleven state-of-the-art methods on five public synthetic and one real-world dataset.
arXiv Detail & Related papers (2022-02-14T09:09:08Z) - Structure-Preserving Deraining with Residue Channel Prior Guidance [33.41254475191555]
Single image deraining is important for many high-level computer vision tasks.
We propose a Structure-Preserving Deraining Network (SPDNet) with RCP guidance.
SPDNet directly generates high-quality rain-free images with clear and accurate structures under RCP guidance.
arXiv Detail & Related papers (2021-08-20T09:09:56Z) - RCDNet: An Interpretable Rain Convolutional Dictionary Network for
Single Image Deraining [49.99207211126791]
We specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet)
RCDNet embeds the intrinsic priors of rain streaks and has clear interpretability.
By end-to-end training such an interpretable network, all involved rain kernels and proximal operators can be automatically extracted.
arXiv Detail & Related papers (2021-07-14T16:08:11Z) - Beyond Monocular Deraining: Parallel Stereo Deraining Network Via
Semantic Prior [103.49307603952144]
Most existing de-rain algorithms use only one single input image and aim to recover a clean image.
We present a Paired Rain Removal Network (PRRNet), which exploits both stereo images and semantic information.
Experiments on both monocular and the newly proposed stereo rainy datasets demonstrate that the proposed method achieves the state-of-the-art performance.
arXiv Detail & Related papers (2021-05-09T04:15:10Z) - Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop
Removal [103.4067418083549]
We propose a Dual Attention-in-Attention Model (DAiAM) which includes two DAMs for removing both rain streaks and raindrops simultaneously.
The proposed method not only is capable of removing rain streaks and raindrops simultaneously, but also achieves the state-of-the-art performance on both tasks.
arXiv Detail & Related papers (2021-03-12T03:00:33Z) - A Model-driven Deep Neural Network for Single Image Rain Removal [52.787356046951494]
We propose a model-driven deep neural network for the task, with fully interpretable network structures.
Based on the convolutional dictionary learning mechanism for representing rain, we propose a novel single image deraining model.
All the rain kernels and operators can be automatically extracted, faithfully characterizing the features of both rain and clean background layers.
arXiv Detail & Related papers (2020-05-04T09:13:25Z) - Physical Model Guided Deep Image Deraining [10.14977592107907]
Single image deraining is an urgent task because the degraded rainy image makes many computer vision systems fail to work.
We propose a novel network based on physical model guided learning for single image deraining.
arXiv Detail & Related papers (2020-03-30T07:08:13Z) - Multi-Task Learning Enhanced Single Image De-Raining [9.207797392774465]
Rain removal in images is an important task in computer vision filed and attracting attentions of more and more people.
In this paper, we address a non-trivial issue of removing visual effect of rain streak from a single image.
Our method combines various semantic constraint task in a proposed multi-task regression model for rain removal.
arXiv Detail & Related papers (2020-03-21T16:19:56Z) - Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network [45.78251508028359]
We propose a new semi-supervised GAN-based deraining network termed Semi-DerainGAN.
It can use both synthetic and real rainy images in a uniform network using two supervised and unsupervised processes.
To deliver better deraining results, we design a paired discriminator for distinguishing the real pairs from fake pairs.
arXiv Detail & Related papers (2020-01-23T07:01:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.