Semi-supervised Transfer Learning for Image Rain Removal. In CVPR 2019.

Related tags

Deep Learning Semi-supervised-IRR
Overview

Semi-supervised Transfer Learning for Image Rain Removal

This package contains the Python implementation of "Semi-supervised Transfer Learning for Image Rain Removal", in CVPR 2019.

Usage

Prepare Training Data

Download synthesized data from here, as supervised training data. Put input images in './data/rainy_image_dataset/input' and ground truth images in './data/rainy_image_dataset/label'. Run /data/generate.m to generate HDF files as training data.

Train

python training.py

Test

python testing.py

Cite

Please cite this paper if you use the code:

@InProceedings{Wei_2019_CVPR,
author = {Wei, Wei and Meng, Deyu and Zhao, Qian and Xu, Zongben and Wu, Ying},
title = {Semi-Supervised Transfer Learning for Image Rain Removal},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition},
year = {2019}
}

The prior work using MoG for video deraining can be found here:

@InProceedings{wei2017should,
  title={Should we encode rain streaks in video as deterministic or stochastic?},
  author={Wei, Wei and Yi, Lixuan and Xie, Qi and Zhao, Qian and Meng, Deyu and Xu, Zongben},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year={2017}
}

Acknowledge

We use Deep Detail Network as our baseline. Thanks for sharing the code!

Note

  1. You are welcomed to add more real data.
  2. You are welcomed to try more recent derain network as baseline.
You might also like...
UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning
UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning This is the official PyTorch implementation for UniMoCo pape

dddzg 49 Jan 2, 2023
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC) Xin Lai*, Zhuotao Tian*, Li Jiang, Shu Liu, Hengshuang Zhao, Li

Jia Research Lab 137 Dec 14, 2022
[CVPR 2021] MiVOS - Mask Propagation module. Reproduced STM (and better) with training code :star2:. Semi-supervised video object segmentation evaluation.
[CVPR 2021] MiVOS - Mask Propagation module. Reproduced STM (and better) with training code :star2:. Semi-supervised video object segmentation evaluation.

MiVOS (CVPR 2021) - Mask Propagation Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang [arXiv] [Paper PDF] [Project Page] [Papers with Code] This repo impleme

Rex Cheng 106 Jan 3, 2023
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation (CVPR 2021)
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation (CVPR 2021)

Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation Input Image Initial CAM Successive Maps with adversar

Jungbeom Lee 110 Dec 7, 2022
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC) Xin Lai*, Zhuotao Tian*, Li Jiang, Shu Liu, Hengshuang Zhao, Li

DV Lab 137 Dec 14, 2022
[CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
[CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

TorchSemiSeg [CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision by Xiaokang Chen1, Yuhui Yuan2, Gang Zeng1, Jingdong Wang

Chen XiaoKang 387 Jan 8, 2023
[CVPR 2022] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
[CVPR 2022] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

Using Unreliable Pseudo Labels Official PyTorch implementation of Semi-Supervised Semantic Segmentation Using Unreliable Pseudo Labels, CVPR 2022. Ple

Haochen Wang 268 Dec 24, 2022
Hybrid CenterNet - Hybrid-supervised object detection / Weakly semi-supervised object detection
Hybrid CenterNet - Hybrid-supervised object detection / Weakly semi-supervised object detection

Hybrid-Supervised Object Detection System Object detection system trained by hybrid-supervision/weakly semi-supervision (HSOD/WSSOD): This project is

null 5 Dec 10, 2022
Transfer style api - An API to use with Tranfer Style App, where you can use two image and transfer the style

Transfer Style API It's an API to use with Tranfer Style App, where you can use

Brian Alejandro 1 Feb 13, 2022
Comments
  • A blank image is obtained as output

    A blank image is obtained as output

    I am getting a blank image as output. It seems to be so because of the following lines in the code. final_output[np.where(final_output < 0. )] = 0. final_output[np.where(final_output > 1. )] = 1. derained = final_output[0,:,:,:]

    The final_output 3D array I am getting has values greater than 1. It's something like this: Screenshot (132) The code mentioned above converts it into an array of ones. Screenshot (133) Can you please suggest how to get the expected output?

    opened by AnwesaBhattacharya 2
Owner
Wei Wei
Wei Wei
GitHub
RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020)

RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020) Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng [PDF] [Supplementary M

Hong Wang 6 Sep 27, 2022
Self-Learned Video Rain Streak Removal: When Cyclic Consistency Meets Temporal Correspondence

In this paper, we address the problem of rain streaks removal in video by developing a self-learned rain streak removal method, which does not require any clean groundtruth images in the training process.

Yang Wenhan 44 Dec 6, 2022
《Single Image Reflection Removal Beyond Linearity》(CVPR 2019)

Single-Image-Reflection-Removal-Beyond-Linearity Paper Single Image Reflection Removal Beyond Linearity. Qiang Wen, Yinjie Tan, Jing Qin, Wenxi Liu, G

Qiang Wen 51 Jun 24, 2022
MPRNet-Cloud-removal: Progressive cloud removal

MPRNet-Cloud-removal Progressive cloud removal Requirements 1.Pytorch >= 1.0 2.Python 3 3.NVIDIA GPU + CUDA 9.0 4.Tensorboard Installation 1.Clone the

Semi 95 Dec 18, 2022
A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

SEAL ⠀⠀⠀ A PyTorch implementation of Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019) Abstract Node classification an

Benedek Rozemberczki 202 Dec 27, 2022
Official pytorch code for SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal

SSAT: A Symmetric Semantic-Aware Transformer Network for Makeup Transfer and Removal This is the official pytorch code for SSAT: A Symmetric Semantic-

ForeverPupil 57 Dec 13, 2022
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). A PyTorch implementation.

Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set —— PyTorch implementation This is an unofficial offici

Sicheng Xu 833 Dec 28, 2022
Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

Tom-R.T.Kvalvaag 2 Dec 17, 2021
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement (CVPR'2020)

Under-exposure introduces a series of visual degradation, i.e. decreased visibility, intensive noise, and biased color, etc. To address these problems, we propose a novel semi-supervised learning approach for low-light image enhancement.

Yang Wenhan 117 Jan 3, 2023
Self-Supervised Methods for Noise-Removal

SSMNR | Self-Supervised Methods for Noise Removal Image denoising is the task of removing noise from an image, which can be formulated as the task of

null 1 Jan 16, 2022