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Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms

Title
Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms
Authors
Rim, JaesungLee, HaeyunWon, JucheolCho, Sunghyun
Issue Date
2020-08-25
Citation
16th European Conference on Computer Vision, ECCV 2020, 184-201
Type
Conference
ISBN
9783030585945
ISSN
0302-9743
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. © 2020, Springer Nature Switzerland AG.
URI
http://hdl.handle.net/20.500.11750/12902
DOI
10.1007/978-3-030-58595-2_12
Publisher
Springer Science and Business Media Deutschland GmbH
Files:
There are no files associated with this item.
Collection:
ETC2. Conference Papers


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