Reflection Removal via Realistic Training Data Generation

Youxin Pang; Mengke Yuan; Qiang Fu; Dong Ming Yan

doi:10.1145/3388770.3407419

Reflection Removal via Realistic Training Data Generation

Youxin Pang, Mengke Yuan, Qiang Fu, Dong Ming Yan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

We present a valid polarization-based reflection contaminated image synthesis method, which can provide adequate, diverse and authentic training dataset. Meanwhile, we enhance the neural network by introducing the reflection information as guidance and utilizing adaptive convolution kernel size to fuse multi-scale information. We demonstrate that the proposed approach achieves convincing improvements over state of the arts.

Original language	English (US)
Title of host publication	ACM SIGGRAPH 2020 Posters
Publisher	ACM
ISBN (Print)	9781450379731
DOIs	https://doi.org/10.1145/3388770.3407419
State	Published - Aug 15 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-15
Acknowledgements: This work was supported by the National Key R&D Program of China (2019YFB2204104 and 2018YFB2100602). (Portions of) the research in this paper used the 'SIR2' Dataset made available by the ROSE Lab at the Nanyang Technological University, Singapore.

Access to Document

10.1145/3388770.3407419

Cite this

@inproceedings{153ee76f3f8b40dba2f1cdfd5ffd7866,

title = "Reflection Removal via Realistic Training Data Generation",

abstract = "We present a valid polarization-based reflection contaminated image synthesis method, which can provide adequate, diverse and authentic training dataset. Meanwhile, we enhance the neural network by introducing the reflection information as guidance and utilizing adaptive convolution kernel size to fuse multi-scale information. We demonstrate that the proposed approach achieves convincing improvements over state of the arts.",

author = "Youxin Pang and Mengke Yuan and Qiang Fu and Yan, {Dong Ming}",

note = "KAUST Repository Item: Exported on 2020-10-15 Acknowledgements: This work was supported by the National Key R&D Program of China (2019YFB2204104 and 2018YFB2100602). (Portions of) the research in this paper used the 'SIR2' Dataset made available by the ROSE Lab at the Nanyang Technological University, Singapore.",

year = "2020",

month = aug,

day = "15",

doi = "10.1145/3388770.3407419",

language = "English (US)",

isbn = "9781450379731",

booktitle = "ACM SIGGRAPH 2020 Posters",

publisher = "ACM",

}

TY - GEN

T1 - Reflection Removal via Realistic Training Data Generation

AU - Pang, Youxin

AU - Yuan, Mengke

AU - Fu, Qiang

AU - Yan, Dong Ming

N1 - KAUST Repository Item: Exported on 2020-10-15 Acknowledgements: This work was supported by the National Key R&D Program of China (2019YFB2204104 and 2018YFB2100602). (Portions of) the research in this paper used the 'SIR2' Dataset made available by the ROSE Lab at the Nanyang Technological University, Singapore.

PY - 2020/8/15

Y1 - 2020/8/15

N2 - We present a valid polarization-based reflection contaminated image synthesis method, which can provide adequate, diverse and authentic training dataset. Meanwhile, we enhance the neural network by introducing the reflection information as guidance and utilizing adaptive convolution kernel size to fuse multi-scale information. We demonstrate that the proposed approach achieves convincing improvements over state of the arts.

AB - We present a valid polarization-based reflection contaminated image synthesis method, which can provide adequate, diverse and authentic training dataset. Meanwhile, we enhance the neural network by introducing the reflection information as guidance and utilizing adaptive convolution kernel size to fuse multi-scale information. We demonstrate that the proposed approach achieves convincing improvements over state of the arts.

UR - http://hdl.handle.net/10754/665577

UR - https://dl.acm.org/doi/10.1145/3388770.3407419

UR - http://www.scopus.com/inward/record.url?scp=85091969870&partnerID=8YFLogxK

U2 - 10.1145/3388770.3407419

DO - 10.1145/3388770.3407419

M3 - Conference contribution

SN - 9781450379731

BT - ACM SIGGRAPH 2020 Posters

PB - ACM

ER -

Reflection Removal via Realistic Training Data Generation

Abstract

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this