NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
Goutam Bhat, Martin Danelljan, Radu Timofte, Kazutoshi Akita, and Wooyeong Cho, Haoqiang Fan, Lanpeng Jia, Daeshik Kim, Bruno, Lecouat, Youwei Li, Shuaicheng Liu, Ziluan Liu, Ziwei Luo and, Takahiro Maeda, Julien Mairal, Christian Micheloni, Xuan Mo and, Takeru Oba

TL;DR
This paper reviews the NTIRE2021 challenge on burst super-resolution, highlighting diverse methods that achieved state-of-the-art results in generating high-resolution images from noisy bursts.
Contribution
It presents a comprehensive overview of challenge methods and results, establishing new benchmarks for burst super-resolution performance.
Findings
Top methods set new state-of-the-art performance.
Diverse solutions effectively enhanced image resolution.
Real-world and synthetic data were both used for evaluation.
Abstract
This paper reviews the NTIRE2021 challenge on burst super-resolution. Given a RAW noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times higher resolution. The challenge contained two tracks; Track 1 evaluating on synthetically generated data, and Track 2 using real-world bursts from mobile camera. In the final testing phase, 6 teams submitted results using a diverse set of solutions. The top-performing methods set a new state-of-the-art for the burst super-resolution task.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Processing Techniques and Applications · Advanced Image Processing Techniques · Advanced Optical Sensing Technologies
