Name
Abstract | ||
|---|---|---|
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 4× 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPRW56347.2022.00117 | IEEE Conference on Computer Vision and Pattern Recognition |
DocType | Volume | Issue |
Conference | 2022 | 1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| g bhat | 1 | 0 | 0.34 |
| Danelljan Martin | 2 | 1344 | 49.35 |
| Radu Timofte | 3 | 1880 | 118.45 |
| Yaolong Cao | 4 | 16 | 7.86 |
| Yaolong Cao | 5 | 16 | 7.86 |
| Chen Ming | 6 | 12 | 11.07 |
| xiaoling chen | 7 | 155 | 27.17 |
| Chengyong Su | 8 | 0 | 0.68 |