liu.seSearch for publications in DiVA
Operational message
There are currently operational disruptions. Troubleshooting is in progress.
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
NTIRE 2022 Burst Super-Resolution Challenge
Swiss Fed Inst Technol, Switzerland.
Swiss Fed Inst Technol, Switzerland.
Swiss Fed Inst Technol, Switzerland; Julius Maximilian Univ Wurzburg, Germany.
Commun Univ China, Peoples R China.
Show others and affiliations
2022 (English)In: 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2022), IEEE , 2022, p. 1040-1060Conference paper, Published paper (Refereed)
Abstract [en]

Burst super-resolution has received increased attention in recent years due to its applications in mobile photography. By merging information from multiple shifted images of a scene, burst super-resolution aims to recover details which otherwise cannot be obtained using a simple input image. This paper reviews the NTIRE 2022 challenge on burst super-resolution. In the challenge, the participants were tasked with generating a clean RGB image with 4x higher resolution, given a RAW noisy burst as input. That is, the methods need to perform joint denoising, demosaicking, and super-resolution. The challenge consisted of 2 tracks. Track 1 employed synthetic data, where pixel-accurate high-resolution ground truths are available. Track 2 on the other hand used real-world bursts captured from a handheld camera, along with approximately aligned reference images captured using a DSLR. 14 teams participated in the final testing phase. The top performing methods establish a new state-of-the-art on the burst super-resolution task.

Place, publisher, year, edition, pages
IEEE , 2022. p. 1040-1060
National Category
Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:liu:diva-190497DOI: 10.1109/CVPRW56347.2022.00117ISI: 000861612701012ISBN: 9781665487399 (electronic)ISBN: 9781665487405 (print)OAI: oai:DiVA.org:liu-190497DiVA, id: diva2:1718558
Conference
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, jun 18-24, 2022
Note

Funding Agencies|Huawei; Reality Labs; Bending Spoons; MediaTek; OPPO; Oddity; Voyage81; ETH Zurich (Computer Vision Lab); University of Wurzburg (CAIDAS)

Available from: 2022-12-13 Created: 2022-12-13 Last updated: 2025-02-07

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Khan, Fahad
By organisation
Computer VisionFaculty of Science & Engineering
Computer graphics and computer vision

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 214 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf