%0 Journal Article %T Raindrop Removal With Light Field Image Using Image Inpainting %+ Connaissance et Intelligence Artificielle Distribuées [Dijon] (CIAD) %+ School of Astronautics, Northwestern Polytechnical University Xi’an %+ University of Chinese Academy of Sciences [Beijing] (UCAS) %+ Xi'an Institute of Optics and Precision Mechanics %+ Artificial Intelligence Center, Czech Technical University in Prague %A Yang, Tao %A Chang, Xiaofei %A Su, Hang %A Crombez, Nathan %A Ruichek, Yassine %A Krajnik, Tomas %A Yan, Zhi %Z Natural Science Foundation of Shaanxi Province2019ZY-CXPT-03 %Z PHC Barrande Programme 40682ZH (3L4AV) %Z CSF 17-27006Y20-27034J %Z CZ MSMT Projects FR-8J18FR018 %< avec comité de lecture %@ 2169-3536 %J IEEE Access %I IEEE %V 8 %P 58416-58426 %8 2020 %D 2020 %R 10.1109/ACCESS.2020.2981641 %K light field %K image inpainting %Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Journal articles %X 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. %G English %L hal-02887542 %U https://u-bourgogne.hal.science/hal-02887542 %~ UNIV-BOURGOGNE %~ UNIV-BM %~ UNIV-BM-THESE %~ CIAD