An Image Inpainting Method Considering Edge Connectivity of Defects

Marika Arimoto; Junichi Hara; Hiroshi Watanabe

doi:10.1109/GCCE53005.2021.9621917

An Image Inpainting Method Considering Edge Connectivity of Defects

Marika Arimoto, Junichi Hara, Hiroshi Watanabe

School of Fundamental Science and Engineering

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

Abstract

In this paper, we propose a new image inpainting method that improves the line distortion and unnatural coloration in the restoration area observed in the conventional methods. Many deep learning inpainting methods have been proposed in recent years. However, these conventional methods have problems with distorted lines and unnatural colors in the restoration area. We solve the problems of the reconstruction of distorted lines and unnatural color simultaneously by applying edge generator from EdgeConnect to DeepFill v2. Through evaluation experiments, we show that the proposed model is better than the conventional methods in PSNR and SSIM of images with clear color boundaries and complex edges.

Original language	English
Title of host publication	2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	101-102
Number of pages	2
ISBN (Electronic)	9781665436762
DOIs	https://doi.org/10.1109/GCCE53005.2021.9621917
Publication status	Published - 2021
Event	10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan Duration: 2021 Oct 12 → 2021 Oct 15

Publication series

Name	2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference	10th IEEE Global Conference on Consumer Electronics, GCCE 2021
Country/Territory	Japan
City	Kyoto
Period	21/10/12 → 21/10/15

Keywords

image inpainting
image restoration

ASJC Scopus subject areas

Computer Science Applications
Signal Processing
Biomedical Engineering
Electrical and Electronic Engineering
Media Technology
Instrumentation

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10.1109/GCCE53005.2021.9621917

Cite this

Arimoto, M., Hara, J., & Watanabe, H. (2021). An Image Inpainting Method Considering Edge Connectivity of Defects. In 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021 (pp. 101-102). (2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE53005.2021.9621917

An Image Inpainting Method Considering Edge Connectivity of Defects. / Arimoto, Marika; Hara, Junichi; Watanabe, Hiroshi.

2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 101-102 (2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021).

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

Arimoto, M, Hara, J & Watanabe, H 2021, An Image Inpainting Method Considering Edge Connectivity of Defects. in 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021. 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021, Institute of Electrical and Electronics Engineers Inc., pp. 101-102, 10th IEEE Global Conference on Consumer Electronics, GCCE 2021, Kyoto, Japan, 21/10/12. https://doi.org/10.1109/GCCE53005.2021.9621917

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abstract = "In this paper, we propose a new image inpainting method that improves the line distortion and unnatural coloration in the restoration area observed in the conventional methods. Many deep learning inpainting methods have been proposed in recent years. However, these conventional methods have problems with distorted lines and unnatural colors in the restoration area. We solve the problems of the reconstruction of distorted lines and unnatural color simultaneously by applying edge generator from EdgeConnect to DeepFill v2. Through evaluation experiments, we show that the proposed model is better than the conventional methods in PSNR and SSIM of images with clear color boundaries and complex edges.",

keywords = "image inpainting, image restoration",

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year = "2021",

doi = "10.1109/GCCE53005.2021.9621917",

language = "English",

series = "2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021",

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N2 - In this paper, we propose a new image inpainting method that improves the line distortion and unnatural coloration in the restoration area observed in the conventional methods. Many deep learning inpainting methods have been proposed in recent years. However, these conventional methods have problems with distorted lines and unnatural colors in the restoration area. We solve the problems of the reconstruction of distorted lines and unnatural color simultaneously by applying edge generator from EdgeConnect to DeepFill v2. Through evaluation experiments, we show that the proposed model is better than the conventional methods in PSNR and SSIM of images with clear color boundaries and complex edges.

AB - In this paper, we propose a new image inpainting method that improves the line distortion and unnatural coloration in the restoration area observed in the conventional methods. Many deep learning inpainting methods have been proposed in recent years. However, these conventional methods have problems with distorted lines and unnatural colors in the restoration area. We solve the problems of the reconstruction of distorted lines and unnatural color simultaneously by applying edge generator from EdgeConnect to DeepFill v2. Through evaluation experiments, we show that the proposed model is better than the conventional methods in PSNR and SSIM of images with clear color boundaries and complex edges.

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An Image Inpainting Method Considering Edge Connectivity of Defects

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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