Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset (CVPR'19)

Overview

Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset (CVPR'19)

Tianyu Wang*, Xin Yang*, Ke Xu, Shaozhe Chen, Qiang Zhang, Rynson W.H. Lau † (* Joint first author. † Rynson Lau is the corresponding author.)

[Arxiv]

Abstract

Removing rain streaks from a single image has been drawing considerable attention as rain streaks can severely degrade the image quality and affect the performance of existing outdoor vision tasks. While recent CNN-based derainers have reported promising performances, deraining remains an open problem for two reasons. First, existing synthesized rain datasets have only limited realism, in terms of modeling real rain characteristics such as rain shape, direction and intensity. Second, there are no public benchmarks for quantitative comparisons on real rain images, which makes the current evaluation less objective. The core challenge is that real world rain/clean image pairs cannot be captured at the same time. In this paper, we address the single image rain removal problem in two ways. First, we propose a semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images. Using this method, we construct a large-scale dataset of ∼29.5K rain/rain-free image pairs that cover a wide range of natural rain scenes. Second, to better cover the stochastic distributions of real rain streaks, we propose a novel SPatial Attentive Network (SPANet) to remove rain streaks in a local-to-global manner. Extensive experiments demonstrate that our network performs favorably against the state-of-the-art deraining methods.

Citation

If you use this code or our dataset(including test set), please cite:

@InProceedings{Wang_2019_CVPR,
  author = {Wang, Tianyu and Yang, Xin and Xu, Ke and Chen, Shaozhe and Zhang, Qiang and Lau, Rynson W.H.},
  title = {Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2019}
}

Dataset

See my personal site

UPDATE We release the code of clean image generation. We also provide some synthesize and real video examples for researchers to try. Note that we only implemented the code using 8 threads.

Requirements

  • PyTorch == 0.4.1 (1.0.x may not work for training)
  • cupy (Installation Guide)
  • opencv-python
  • TensorBoardX
  • Python3.6
  • progressbar2
  • scikit-image
  • ffmpeg >= 4.0.1
  • python-ffmpeg

Setup

  • Clone this repo:
$ git clone ...
$ cd SPANet

Train & Test

Train:

  • Download the dataset(~44GB) and unpack it into code folder (See details in Train_Dataset_README.md). Then, run:
$ python main.py -a train -m latest

Test:

  • Download the test dataset(~455MB) and unpack it into code folder (See details in Test_Dataset_README.md). Then, run:
$ python main.py -a test -m latest

Performance Change

PSNR 38.02 -> 38.53

SSIM 0.9868 -> 0.9875

For generalization, we here stop at 40K steps.

All PSNR and SSIM of results are computed by using skimage.measure. Please use this to evaluate your works.

License

Please see License.txt file.

Acknowledgement

Code borrows from RESCAN by Xia Li. The CUDA extension references pyinn by Sergey Zagoruyko and DSC(CF-Caffe) by Xiaowei Hu. Thanks for sharing!

Contact

E-Mail: [email protected]

Owner
Steve Wong
Discovering the world. CS Ph.D @ CUHK
Steve Wong
Implementation of Nyström Self-attention, from the paper Nyströmformer

Nyström Attention Implementation of Nyström Self-attention, from the paper Nyströmformer. Yannic Kilcher video Install $ pip install nystrom-attention

Phil Wang 95 Jan 02, 2023
Optical machine for senses sensing using speckle and deep learning

# Senses-speckle [Remote Photonic Detection of Human Senses Using Secondary Speckle Patterns](https://doi.org/10.21203/rs.3.rs-724587/v1) paper Python

Zeev Kalyuzhner 0 Sep 26, 2021
[CVPR 2020] Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

Contents Local and Global GAN Cross-View Image Translation Semantic Image Synthesis Acknowledgments Related Projects Citation Contributions Collaborat

Hao Tang 131 Dec 07, 2022
Joint learning of images and text via maximization of mutual information

mutual_info_img_txt Joint learning of images and text via maximization of mutual information. This repository incorporates the algorithms presented in

Ruizhi Liao 10 Dec 22, 2022
Computational inteligence project on faces in the wild dataset

Table of Contents The general idea How these scripts work? Loading data Needed modules and global variables Parsing the arrays in dataset Extracting a

tooraj taraz 4 Oct 21, 2022
YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931)

Introduction Yolov5-face is a real-time,high accuracy face detection. Performance Single Scale Inference on VGA resolution(max side is equal to 640 an

DeepCam Shenzhen 1.4k Jan 07, 2023
A curated list of awesome papers for Semantic Retrieval (TOIS Accepted: Semantic Models for the First-stage Retrieval: A Comprehensive Review).

A curated list of awesome papers for Semantic Retrieval (TOIS Accepted: Semantic Models for the First-stage Retrieval: A Comprehensive Review).

Yinqiong Cai 189 Dec 28, 2022
[arXiv'22] Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation

Panoptic NeRF Project Page | Paper | Dataset Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation Xiao Fu*, Shangzhan zhang*,

Xiao Fu 111 Dec 16, 2022
Cycle Consistent Adversarial Domain Adaptation (CyCADA)

Cycle Consistent Adversarial Domain Adaptation (CyCADA) A pytorch implementation of CyCADA. If you use this code in your research please consider citi

Hyunwoo Ko 2 Jan 10, 2022
Implementations of the algorithms in the paper Approximative Algorithms for Multi-Marginal Optimal Transport and Free-Support Wasserstein Barycenters

Implementations of the algorithms in the paper Approximative Algorithms for Multi-Marginal Optimal Transport and Free-Support Wasserstein Barycenters

Johannes von Lindheim 3 Oct 29, 2022
BirdCLEF 2021 - Birdcall Identification 4th place solution

BirdCLEF 2021 - Birdcall Identification 4th place solution My solution detail kaggle discussion Inference Notebook (best submission) Environment Use K

tattaka 42 Jan 02, 2023
Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification tasks

Uniformer - Pytorch Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification ta

Phil Wang 90 Nov 24, 2022
Where2Act: From Pixels to Actions for Articulated 3D Objects

Where2Act: From Pixels to Actions for Articulated 3D Objects The Proposed Where2Act Task. Given as input an articulated 3D object, we learn to propose

Kaichun Mo 69 Nov 28, 2022
Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data

SEDE SEDE (Stack Exchange Data Explorer) is new dataset for Text-to-SQL tasks with more than 12,000 SQL queries and their natural language description

Rupert. 83 Nov 11, 2022
This Deep Learning Model Predicts that from which disease you are suffering.

Deep-Learning-Project This Deep Learning Model Predicts that from which disease you are suffering. This Project Covers the Topics of Deep Learning Int

Jai Viral Doshi 0 Jan 20, 2022
Official implementation of UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation

UTNet (Accepted at MICCAI 2021) Official implementation of UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation Introduction Transf

110 Jan 01, 2023
Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains

Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains This is an accompanying repository to the ICAIL 2021 pap

4 Dec 16, 2021
Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation

GPT2-Pytorch with Text-Generator Better Language Models and Their Implications Our model, called GPT-2 (a successor to GPT), was trained simply to pre

Tae-Hwan Jung 775 Jan 08, 2023
a Pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021"

A pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in 2021" 1. Notes This is a pytorch easy re-implement of "YOLOX: Exceeding YOLO Series in

91 Dec 26, 2022
This package implements THOR: Transformer with Stochastic Experts.

THOR: Transformer with Stochastic Experts This PyTorch package implements Taming Sparsely Activated Transformer with Stochastic Experts. Installation

Microsoft 45 Nov 22, 2022