Video Reflection Removal by Modified EDVR and 3D Convolution

Sota MORIYAMA; Koichi ICHIGE; Yuichi HORI; Masayuki TACHI

doi:10.1587/transfun.2023EAL2078

Sota MORIYAMA, Koichi ICHIGE, Yuichi HORI, Masayuki TACHI

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Keywords: video reflection removal, video restoration, machine learning, convolutional neural network, deformable convolutions, 3D convolutions

JOURNAL FREE ACCESS Advance online publication

Article ID: 2023EAL2078

DOI https://doi.org/10.1587/transfun.2023EAL2078

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Abstract

In this paper, we propose a method for video reflection removal using a video restoration framework with enhanced deformable networks (EDVR). We examine the effect of each module in EDVR on video reflection removal and modify the models using 3D convolutions. The performance of each modified model is evaluated in terms of the RMSE between the structural similarity (SSIM) and the smoothed SSIM representing temporal consistency.

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