Density-aware Single Image De-raining using a Multi-stream Dense Network (CVPR 2018)

Overview

DID-MDN

Density-aware Single Image De-raining using a Multi-stream Dense Network

He Zhang, Vishal M. Patel

[Paper Link] (CVPR'18)

We present a novel density-aware multi-stream densely connected convolutional neural network-based algorithm, called DID-MDN, for joint rain density estimation and de-raining. The proposed method enables the network itself to automatically determine the rain-density information and then efficiently remove the corresponding rain-streaks guided by the estimated rain-density label. To better characterize rain-streaks with dif- ferent scales and shapes, a multi-stream densely connected de-raining network is proposed which efficiently leverages features from different scales. Furthermore, a new dataset containing images with rain-density labels is created and used to train the proposed density-aware network.

@inproceedings{derain_zhang_2018,		
  title={Density-aware Single Image De-raining using a Multi-stream Dense Network},
  author={Zhang, He and Patel, Vishal M},
  booktitle={CVPR},
  year={2018}
} 

Prerequisites:

  1. Linux
  2. Python 2 or 3
  3. CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0)

Installation:

  1. Install PyTorch and dependencies from http://pytorch.org (Ubuntu+Python2.7) (conda install pytorch torchvision -c pytorch)

  2. Install Torch vision from the source. (git clone https://github.com/pytorch/vision cd vision python setup.py install)

  3. Install python package: numpy, scipy, PIL, pdb

Demo using pre-trained model

python test.py --dataroot ./facades/github --valDataroot ./facades/github --netG ./pre_trained/netG_epoch_9.pth   

Pre-trained model can be downloaded at (put it in the folder 'pre_trained'): https://drive.google.com/drive/folders/1VRUkemynOwWH70bX9FXL4KMWa4s_PSg2?usp=sharing

Pre-trained density-aware model can be downloaded at (Put it in the folder 'classification'): https://drive.google.com/drive/folders/1-G86JTvv7o1iTyfB2YZAQTEHDtSlEUKk?usp=sharing

Pre-trained residule-aware model can be downloaded at (Put it in the folder 'residual_heavy'): https://drive.google.com/drive/folders/1bomrCJ66QVnh-WduLuGQhBC-aSWJxPmI?usp=sharing

Training (Density-aware Deraining network using GT label)

python derain_train_2018.py  --dataroot ./facades/DID-MDN-training/Rain_Medium/train2018new  --valDataroot ./facades/github --exp ./check --netG ./pre_trained/netG_epoch_9.pth.
Make sure you download the training sample and put in the right folder

Density-estimation Training (rain-density classifier)

python train_rain_class.py  --dataroot ./facades/DID-MDN-training/Rain_Medium/train2018new  --exp ./check_class	

Testing

python demo.py --dataroot ./your_dataroot --valDataroot ./your_dataroot --netG ./pre_trained/netG_epoch_9.pth   

Reproduce

To reproduce the quantitative results shown in the paper, please save both generated and target using python demo.py into the .png format and then test using offline tool such as the PNSR and SSIM measurement in Python or Matlab. In addition, please use netG.train() for testing since the batch for training is 1.

Dataset

Training (heavy, medium, light) and testing (TestA and Test B) data can be downloaded at the following link: https://drive.google.com/file/d/1cMXWICiblTsRl1zjN8FizF5hXOpVOJz4/view?usp=sharing

License

Code is under MIT license.

Acknowledgments

Great thanks for the insight discussion with Vishwanath Sindagi and help from Hang Zhang

Owner
He Zhang
Research Sc[email protected], Phd in Computer Vision, Deep Learning
He Zhang
Code for paper "Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs"

This is the codebase for the paper: Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs Directory Structur

Peter Hase 19 Aug 21, 2022
CVPR 2021

Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-image Translation [Paper] | [Poster] | [Codes] Yahui Liu1,3, Enver Sangineto1,

Yahui Liu 37 Sep 12, 2022
AI assistant built in python.the features are it can display time,say weather,open-google,youtube,instagram.

AI assistant built in python.the features are it can display time,say weather,open-google,youtube,instagram.

AK-Shanmugananthan 1 Nov 29, 2021
TAUFE: Task-Agnostic Undesirable Feature DeactivationUsing Out-of-Distribution Data

A deep neural network (DNN) has achieved great success in many machine learning tasks by virtue of its high expressive power. However, its prediction can be easily biased to undesirable features, whi

KAIST Data Mining Lab 8 Dec 07, 2022
PyTorch reimplementation of minimal-hand (CVPR2020)

Minimal Hand Pytorch Unofficial PyTorch reimplementation of minimal-hand (CVPR2020). you can also find in youtube or bilibili bare hand youtube or bil

Hao Meng 228 Dec 29, 2022
Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)"

Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)" which introduces a new class of deep generative models that gene

Guan-Horng Liu 43 Jan 03, 2023
Lacmus is a cross-platform application that helps to find people who are lost in the forest using computer vision and neural networks.

lacmus The program for searching through photos from the air of lost people in the forest using Retina Net neural nwtwork. The project is being develo

Lacmus Foundation 168 Dec 27, 2022
Official PyTorch implementation for FastDPM, a fast sampling algorithm for diffusion probabilistic models

Official PyTorch implementation for "On Fast Sampling of Diffusion Probabilistic Models". FastDPM generation on CIFAR-10, CelebA, and LSUN datasets. S

Zhifeng Kong 68 Dec 26, 2022
The official implementation of paper Siamese Transformer Pyramid Networks for Real-Time UAV Tracking, accepted by WACV22

SiamTPN Introduction This is the official implementation of the SiamTPN (WACV2022). The tracker intergrates pyramid feature network and transformer in

Robotics and Intelligent Systems Control @ NYUAD 28 Nov 25, 2022
Public scripts, services, and configuration for running a smart home K3S network cluster

makerhouse_network Public scripts, services, and configuration for running MakerHouse's home network. This network supports: TODO features here For mo

Scott Martin 1 Jan 15, 2022
Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition"

Code for Two-stage Identifier: "Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition", accepted at ACL 2021. For details of the model and experiments, please see our paper.

tricktreat 87 Dec 16, 2022
Unofficial PyTorch implementation of Attention Free Transformer (AFT) layers by Apple Inc.

aft-pytorch Unofficial PyTorch implementation of Attention Free Transformer's layers by Zhai, et al. [abs, pdf] from Apple Inc. Installation You can i

Rishabh Anand 184 Dec 12, 2022
A user-friendly research and development tool built to standardize RL competency assessment for custom agents and environments.

Built with ❤️ by Sam Showalter Contents Overview Installation Dependencies Usage Scripts Standard Execution Environment Development Environment Benchm

SRI-AIC 1 Nov 18, 2021
Node Dependent Local Smoothing for Scalable Graph Learning

Node Dependent Local Smoothing for Scalable Graph Learning Requirements Environments: Xeon Gold 5120 (CPU), 384GB(RAM), TITAN RTX (GPU), Ubuntu 16.04

Wentao Zhang 15 Nov 28, 2022
Implementation of the master's thesis "Temporal copying and local hallucination for video inpainting".

Temporal copying and local hallucination for video inpainting This repository contains the implementation of my master's thesis "Temporal copying and

David Álvarez de la Torre 1 Dec 02, 2022
A hybrid framework (neural mass model + ML) for SC-to-FC prediction

The current workflow simulates brain functional connectivity (FC) from structural connectivity (SC) with a neural mass model. Gradient descent is applied to optimize the parameters in the neural mass

Yilin Liu 1 Jan 26, 2022
Self-Supervised Learning for Domain Adaptation on Point-Clouds

Self-Supervised Learning for Domain Adaptation on Point-Clouds Introduction Self-supervised learning (SSL) allows to learn useful representations from

Idan Achituve 66 Dec 20, 2022
This repo will contain code to reproduce and build upon understanding transfer learning

What is being transferred in transfer learning? This repo contains the code for the following paper: Behnam Neyshabur*, Hanie Sedghi*, Chiyuan Zhang*.

4 Jun 16, 2021
Attention for PyTorch with Linear Memory Footprint

Attention for PyTorch with Linear Memory Footprint Unofficially implements https://arxiv.org/abs/2112.05682 to get Linear Memory Cost on Attention (+

11 Jan 09, 2022
PyTorchVideo is a deeplearning library with a focus on video understanding work

PyTorchVideo is a deeplearning library with a focus on video understanding work. PytorchVideo provides resusable, modular and efficient components needed to accelerate the video understanding researc

Facebook Research 2.7k Jan 07, 2023