Cited 0 time in webofscience Cited 0 time in scopus

Full metadata record

DC Field Value Language
dc.contributor.advisor 좌훈승 -
dc.contributor.author Rim, Jaesung -
dc.date.accessioned 2020-08-06T06:16:50Z -
dc.date.available 2020-08-06T06:16:50Z -
dc.date.issued 2020 -
dc.identifier.uri http://dgist.dcollection.net/common/orgView/200000333386 en_US
dc.identifier.uri http://hdl.handle.net/20.500.11750/12177 -
dc.description Computational Photography, Deblurring, Low-level Vision, Datasets and Evaluation -
dc.description.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 their corresponding sharp images captured in low-light environments for learning and benchmarking single image deblurring methods. To collect our dataset, we build an image acquisition system to simultaneously capture a geometrically aligned pair of blurred and sharp images, and develop a post-processing method to further align images geometrically and photometrically. We analyze the effect of our post-processing step, and the performance of existing learning-based deblurring methods. Our analysis shows that our dataset significantly improves deblurring quality for real-world low-light images. -
dc.description.statementofresponsibility Y -
dc.description.tableofcontents 1. Introduction 1
2. Related Work 2
3. Image Acquisition System and Process 3
3.1 Image Acquisition System 3
3.2 Image Acquisition Process 4
4. Post-Processing 5
4.1 Downsampling & Denoising 6
4.2 Geometric Alignment 6
4.3 Photometric Alignment 8
5. Experiments 8
5.1 Analysis of RealBlur Dataset 9
5.2 Benchmark 12
6. Conclusion 19
7. Appendix 20
8. References 24
9. 요약문 28
-
dc.format.extent 35 -
dc.language eng -
dc.publisher DGIST -
dc.title Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms -
dc.type Thesis -
dc.identifier.doi https://doi.org/10.22677/thesis.200000333386 -
dc.description.degree Master -
dc.contributor.department Department of Information and Communication Engineering -
dc.contributor.localid 201822022 -
dc.contributor.localid 180124 -
dc.contributor.localid 170118 -
dc.contributor.coadvisor Cho, Sunghyun -
dc.date.awarded 2020/08 -
dc.publisher.location Daegu -
dc.description.database dCollection -
dc.citation XT.ID임73R 202008 -
dc.date.accepted 7/23/20 -
dc.contributor.alternativeDepartment 정보통신융합전공 -
dc.embargo.liftdate 7/23/20 -
dc.contributor.affiliatedAuthor Rim, Jaesung -
dc.contributor.affiliatedAuthor Chwa, Hoon Sung -
dc.contributor.affiliatedAuthor Cho, Sunghyun -
dc.contributor.alternativeName 임재성 -
dc.contributor.alternativeName Chwa, Hoon Sung -
dc.contributor.alternativeName 조성현 -
Files in This Item:
Appears in Collections:
Department of Electrical Engineering and Computer Science Theses Master

qrcode

  • twitter
  • facebook
  • mendeley

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE