Burst Image Super-Resolution with Base Frame Selection

Sanghyun Kim, Minjung Lee, Woohyeok Kim, Deunsol Jung, Jaesung Rim, Sunghyun Cho, Minsu Cho; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5940-5949

Burst image super-resolution has been a topic of active research in recent years due to its ability to obtain a high resolution image 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 cost. The comparative analysis reveals the effectiveness of the non-uniform setting for the practical scenario and our FSN on synthetic-/real- NEBI datasets.