A Joint Gated Convolution Technique and SN-PatchGAN Model Applied in Oil Painting Image Restoration

Kuntian Wang

2023

Abstract

Image inpainting, which involves completing absent areas within an image, is a critical technique for enhancing image quality, preserving cultural heritage, and restoring damaged artworks. Traditional convolutional networks often struggle with irregular masks and multi-channel inputs in inpainting tasks. To address these challenges, this study presents a method that combines gated convolutions and a novel Spectral-Normalized Markovian Discriminator Generative Adversarial Network (SN-PatchGAN). Gated convolutions facilitate dynamic feature selection, ensuring color uniformity and high-quality inpainting. SN-PatchGAN, drawing inspiration from perceptual loss, Generative Adversarial Networks (GANs) driven globally and locally, Markovian Generative Adversarial Networks (MarkovianGANs), and Spectral-Normalized Generative Adversarial Networks (SN-GANs), efficiently handles arbitrary hole shapes. This study is conducted on the Oil Painting Images dataset and the outcomes from the experiment demonstrate the effectiveness of this method compared to two other traditional image inpainting methods. More importantly, it significantly improves the realism and quality of inpainted results, offering new possibilities for oil painting restoration and contributing to various societal aspects like conserving cultural heritage.

Paper Citation

in Harvard Style

Wang K. (2023). A Joint Gated Convolution Technique and SN-PatchGAN Model Applied in Oil Painting Image Restoration. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 492-497. DOI: 10.5220/0012804800003885

in Bibtex Style

@conference{daml23,
author={Kuntian Wang},
title={A Joint Gated Convolution Technique and SN-PatchGAN Model Applied in Oil Painting Image Restoration},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={492-497},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012804800003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - A Joint Gated Convolution Technique and SN-PatchGAN Model Applied in Oil Painting Image Restoration
SN - 978-989-758-705-4
AU - Wang K.
PY - 2023
SP - 492
EP - 497
DO - 10.5220/0012804800003885
PB - SciTePress