《Single Image Reflection Removal Beyond Linearity》(CVPR 2019)

Overview

Single-Image-Reflection-Removal-Beyond-Linearity

Paper

Single Image Reflection Removal Beyond Linearity.

Qiang Wen, Yinjie Tan, Jing Qin, Wenxi Liu, Guoqiang Han, and Shengfeng He*

Requirement

  • Python 3.5
  • PIL
  • OpenCV-Python
  • Numpy
  • Pytorch 0.4.0
  • Ubuntu 16.04 LTS

Reflection Synthesis

cd ./Synthesis
  • Constrcut these new folders for training and testing

    training set: trainA, trainB, trainC(contains real-world reflection images for adversarial loss.)

    testing set: testA(contains the images to be used as reflection.), testB(contains the images to be used as transmission.)

  • To train the synthesis model:

python3 ./train.py --dataroot path_to_dir_for_reflection_synthesis/ --gpu_ids 0 --save_epoch_freq 1 --batchSize 10

or you can directly:

bash ./synthesis_train.sh
  • To test the synthesis model:
python3 ./test.py --dataroot path_to_dir_for_synthesis/ --gpu_ids 0 --which_epoch 130 --how_many 1

or you can directly:

bash ./synthesis_test.sh

Here is the pre-trained model. And to generate the three types of reflection images, you can use these original images which are from perceptual-reflection-removal.

Due to the copyright, the real reflection images are not released here.

Reflection Removal

cd ./Removal
  • Constrcut these new folders for training and testing

    training set: trainA(contains the reflection ground truth.), trainB(contains the transmission ground truth), trainC(contains the images which have the reflection to remove.), trainW(contains the alpha blending mask ground truth.)

    testing set: testB(contains the transmission ground truth), testC(contains the images which have the reflection to remove.)

  • To train the removal model:

python3 ./train.py --dataroot path_to_dir_for_reflection_removal/ --gpu_ids 0 --save_epoch_freq 1 --batchSize 5 --which_type focused

or you can directly:

bash ./removal_train.sh
  • To test the removal model:
python3 ./test.py --dataroot path_to_dir_for_reflection_removal/ --which_type focused --which_epoch 130 --how_many 1

or you can directly:

bash ./removal_test.sh

Here are the pre-trained models which are trained on the three types of synthetic dataset.

Here are the synthetic training set and testing set for reflection removal.

To evaluate on other datasets, please finetune the pre-trained models or re-train a new model on the specific training set.

Acknowledgments

Part of the code is based upon pytorch-CycleGAN-and-pix2pix.

Citation

@InProceedings{Wen_2019_CVPR,
  author = {Wen, Qiang and Tan, Yinjie and Qin, Jing and Liu, Wenxi and Han, Guoqiang and He, Shengfeng},
  title = {Single Image Reflection Removal Beyond Linearity},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2019}
}
Owner
Qiang Wen
Qiang Wen
Repository of the paper Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models at ML4AD @ NeurIPS 2021.

Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models Code and supplementary materials Repository of the p

Daniel Bogdoll 4 Jul 13, 2022
Code for IntraQ, PyTorch implementation of our paper under review

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization paper Requirements Python = 3.7.10 Pytorch == 1.7

1 Nov 19, 2021
Code for "The Intrinsic Dimension of Images and Its Impact on Learning" - ICLR 2021 Spotlight

dimensions Estimating the instrinsic dimensionality of image datasets Code for: The Intrinsic Dimensionaity of Images and Its Impact On Learning - Phi

Phil Pope 41 Dec 10, 2022
Official re-implementation of the Calibrated Adversarial Refinement model described in the paper Calibrated Adversarial Refinement for Stochastic Semantic Segmentation

Official re-implementation of the Calibrated Adversarial Refinement model described in the paper Calibrated Adversarial Refinement for Stochastic Semantic Segmentation

Elias Kassapis 31 Nov 22, 2022
A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

Tom 50 Dec 16, 2022
Repository for RNNs using TensorFlow and Keras - LSTM and GRU Implementation from Scratch - Simple Classification and Regression Problem using RNNs

RNN 01- RNN_Classification Simple RNN training for classification task of 3 signal: Sine, Square, Triangle. 02- RNN_Regression Simple RNN training for

Nahid Ebrahimian 13 Dec 13, 2022
CTRL-C: Camera calibration TRansformer with Line-Classification

CTRL-C: Camera calibration TRansformer with Line-Classification This repository contains the official code and pretrained models for CTRL-C (Camera ca

57 Nov 14, 2022
Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency[ECCV 2020]

Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency(ECCV 2020) This is an official python implementati

304 Jan 03, 2023
Official repository for HOTR: End-to-End Human-Object Interaction Detection with Transformers (CVPR'21, Oral Presentation)

Official PyTorch Implementation for HOTR: End-to-End Human-Object Interaction Detection with Transformers (CVPR'2021, Oral Presentation) HOTR: End-to-

Kakao Brain 114 Nov 28, 2022
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle

DOC | Quick Start | 中文 Breaking News !! 🔥 🔥 🔥 OGB-LSC KDD CUP 2021 winners announced!! (2021.06.17) Super excited to announce our PGL team won TWO

1.5k Jan 06, 2023
Scripts and misc. stuff related to the PortSwigger Web Academy

PortSwigger Web Academy Notes Mostly scripts to automate the exploits. Going in the order of the recomended learning path - starting with SQLi. Commun

pageinsec 17 Dec 30, 2022
A Broad Study on the Transferability of Visual Representations with Contrastive Learning

A Broad Study on the Transferability of Visual Representations with Contrastive Learning This repository contains code for the paper: A Broad Study on

Ashraful Islam 29 Nov 09, 2022
🌊 Online machine learning in Python

In a nutshell River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River's ambition

OnlineML 4k Jan 02, 2023
The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

Website | ArXiv | Get Start | Video PIRenderer The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic

Ren Yurui 261 Jan 09, 2023
Out-of-boundary View Synthesis towards Full-frame Video Stabilization

Out-of-boundary View Synthesis towards Full-frame Video Stabilization Introduction | Update | Results Demo | Introduction This repository contains the

25 Oct 10, 2022
CSD: Consistency-based Semi-supervised learning for object Detection

CSD: Consistency-based Semi-supervised learning for object Detection (NeurIPS 2019) By Jisoo Jeong, Seungeui Lee, Jee-soo Kim, Nojun Kwak Installation

80 Dec 15, 2022
Image Deblurring using Generative Adversarial Networks

DeblurGAN arXiv Paper Version Pytorch implementation of the paper DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks. Our netwo

Orest Kupyn 2.2k Jan 01, 2023
List of content farm sites like g.penzai.com.

内容农场网站清单 Google 中文搜索结果包含了相当一部分的内容农场式条目,比如「小 X 知识网」「小 X 百科网」。此种链接常会 302 重定向其主站,页面内容为自动生成,大量堆叠关键字,揉杂一些爬取到的内容,完全不具可读性和参考价值。 尤为过分的是,该类网站可能有成千上万个分身域名被 Goog

WDMPA 541 Jan 03, 2023
Finetune SSL models for MOS prediction

Finetune SSL models for MOS prediction This is code for our paper under review for ICASSP 2022: "Generalization Ability of MOS Prediction Networks" Er

Yamagishi and Echizen Laboratories, National Institute of Informatics 32 Nov 22, 2022
An AFL implementation with UnTracer (our coverage-guided tracer)

UnTracer-AFL This repository contains an implementation of our prototype coverage-guided tracing framework UnTracer in the popular coverage-guided fuz

113 Dec 17, 2022