Burst Image Super-Resolution with Base Frame Selection
<|reference_start|>Burst Image Super-Resolution with Base Frame Selection: Burst image super-resolution has been a topic of active research in recent years due to its ability to obtain a high-resolution image by using complementary information between multiple frames in the burst. In this work, we explore using burst shots with non-uniform exposures to confront real-world practical scenarios by introducing a new benchmark dataset, dubbed Non-uniformly Exposed Burst Image (NEBI), that includes the burst frames at varying exposure times to obtain a broader range of irradiance and motion characteristics within a scene. As burst shots with non-uniform exposures exhibit varying levels of degradation, fusing information of the burst shots into the first frame as a base frame may not result in optimal image quality. To address this limitation, we propose a Frame Selection Network (FSN) for non-uniform scenarios. This network seamlessly integrates into existing super-resolution methods in a plug-and-play manner with low computational costs. The comparative analysis reveals the effectiveness of the nonuniform setting for the practical scenario and our FSN on synthetic-/real- NEBI datasets.<|reference_end|>
@article{kim2024burst,
    title={Burst Image Super-Resolution with Base Frame Selection},
    author={Sanghyun Kim, Min Jung Lee, Woohyeok Kim, Deunsol Jung, Jaesung Rim,
  Sunghyun Cho, Minsu Cho},
    journal={arXiv preprint arXiv:2406.17869},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17869},
    primaryClass={cs.CV}
}