Rethinking Image Deraining via Rain Streaks and Vapors
- URL: http://arxiv.org/abs/2008.00823v1
- Date: Mon, 3 Aug 2020 12:15:07 GMT
- Title: Rethinking Image Deraining via Rain Streaks and Vapors
- Authors: Yinglong Wang, Yibing Song, Chao Ma, and Bing Zeng
- Abstract summary: Single image deraining regards an input image as a fusion of a background image, a transmission map, rain streaks, and atmosphere light.
We reformulate rain streaks as transmission medium together with vapors to model rain imaging.
- Score: 43.23214658411834
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Single image deraining regards an input image as a fusion of a background
image, a transmission map, rain streaks, and atmosphere light. While advanced
models are proposed for image restoration (i.e., background image generation),
they regard rain streaks with the same properties as background rather than
transmission medium. As vapors (i.e., rain streaks accumulation or fog-like
rain) are conveyed in the transmission map to model the veiling effect, the
fusion of rain streaks and vapors do not naturally reflect the rain image
formation. In this work, we reformulate rain streaks as transmission medium
together with vapors to model rain imaging. We propose an encoder-decoder CNN
named as SNet to learn the transmission map of rain streaks. As rain streaks
appear with various shapes and directions, we use ShuffleNet units within SNet
to capture their anisotropic representations. As vapors are brought by rain
streaks, we propose a VNet containing spatial pyramid pooling (SSP) to predict
the transmission map of vapors in multi-scales based on that of rain streaks.
Meanwhile, we use an encoder CNN named ANet to estimate atmosphere light. The
SNet, VNet, and ANet are jointly trained to predict transmission maps and
atmosphere light for rain image restoration. Extensive experiments on the
benchmark datasets demonstrate the effectiveness of the proposed visual model
to predict rain streaks and vapors. The proposed deraining method performs
favorably against state-of-the-art deraining approaches.
Related papers
- Towards General and Fast Video Derain via Knowledge Distillation [10.614356931086267]
We propose a Rain Review-based General video derain Network via knowledge distillation (named RRGNet)
We design a frame grouping-based encoder-decoder network that makes full use of the temporal information of the video.
To consolidate the network's ability to derain, we design a rain review module to play back data from old tasks for the current model.
arXiv Detail & Related papers (2023-08-10T05:27:43Z) - Why current rain denoising models fail on CycleGAN created rain images
in autonomous driving [1.4831974871130875]
Rain is artificially added to a set of clear-weather condition images using a Generative Adversarial Network (GAN)
This artificial generation of rain images is sufficiently realistic as in 7 out of 10 cases, human test subjects believed the generated rain images to be real.
In a second step, this paired good/bad weather image data is used to train two rain denoising models, one based primarily on a Convolutional Neural Network (CNN) and the other using a Vision Transformer.
arXiv Detail & Related papers (2023-05-22T12:42:32Z) - 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) - UnfairGAN: An Enhanced Generative Adversarial Network for Raindrop
Removal from A Single Image [8.642603456626391]
UnfairGAN is an enhanced generative adversarial network that can utilize prior high-level information, such as edges and rain estimation, to boost deraining performance.
We show that our proposed method is superior to other state-of-the-art approaches of deraining raindrops regarding quantitative metrics and visual quality.
arXiv Detail & Related papers (2021-10-11T18:02:43Z) - 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) - 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-Scale Progressive Fusion Network for Single Image Deraining [84.0466298828417]
Rain streaks in the air appear in various blurring degrees and resolutions due to different distances from their positions to the camera.
Similar rain patterns are visible in a rain image as well as its multi-scale (or multi-resolution) versions.
In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features.
arXiv Detail & Related papers (2020-03-24T17:22:37Z)
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.