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2020 | OriginalPaper | Buchkapitel

Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms

verfasst von : Jaesung Rim, Haeyun Lee, Jucheol Won, Sunghyun Cho

Erschienen in: Computer Vision – ECCV 2020

Verlag: Springer International Publishing

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Abstract

Numerous learning-based approaches to single image deblurring for camera and object motion blurs have recently been proposed. To generalize such approaches to real-world blurs, large datasets of real blurred images and their ground truth sharp images are essential. However, there are still no such datasets, thus all the existing approaches resort to synthetic ones, which leads to the failure of deblurring real-world images. In this work, we present a large-scale dataset of real-world blurred images and ground truth sharp images for learning and benchmarking single image deblurring methods. To collect our dataset, we build an image acquisition system to simultaneously capture geometrically aligned pairs of blurred and sharp images, and develop a postprocessing method to produce high-quality ground truth images. We analyze the effect of our postprocessing method and the performance of existing deblurring methods. Our analysis shows that our dataset significantly improves deblurring quality for real-world blurred images.

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Vorheriges Kapitel Global-and-Local Relative Position Embedding for Unsupervised Video Summarization

Nächstes Kapitel SPARK: Spatial-Aware Online Incremental Attack Against Visual Tracking

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We used the libraw library for decoding and demosaicing raw images.

http://cg.postech.ac.kr/research/realblur/.

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Titel: Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms
verfasst von: Jaesung Rim
Haeyun Lee
Jucheol Won
Sunghyun Cho
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2020
Print ISBN: 978-3-030-58594-5

Electronic ISBN: 978-3-030-58595-2

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-58595-2_12

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