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Article dans une revue

Raindrop Removal With Light Field Image Using Image Inpainting

Abstract : In this paper, we propose a method that removes raindrops with light field image using image inpainting. We first use the depth map generated from light field image to detect raindrop regions which are then expressed as a binary mask. The original image with raindrops is improved by refocusing on the far regions and filtering by a high-pass filter. With the binary mask and the enhanced image, image inpainting is then utilized to eliminate raindrops from the original image. We compare pre-trained models of several deep learning based image inpainting methods. A light field raindrop dataset is released to verify our method. Image quality analysis is performed to evaluate the proposed image restoration method. The recovered images are further applied to object detection and visual localization tasks.
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-02887542
Contributeur : Ciad - Université de Bourgogne <>
Soumis le : jeudi 2 juillet 2020 - 12:08:56
Dernière modification le : mardi 21 juillet 2020 - 09:26:05

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Tao Yang, Xiaofei Chang, Hang Su, Nathan Crombez, Yassine Ruichek, et al.. Raindrop Removal With Light Field Image Using Image Inpainting. IEEE Access, IEEE, 2020, 8, pp.58416-58426. ⟨10.1109/ACCESS.2020.2981641⟩. ⟨hal-02887542⟩

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