Burst Image Super-Resolution with Base Frame Selection
This work addresses a practical scenario in burst image super-resolution for applications like photography, but it is incremental as it builds on existing methods with a new dataset and selection network.
The paper tackles the problem of burst image super-resolution with non-uniform exposures by introducing a new benchmark dataset (NEBI) and a Frame Selection Network (FSN) for selecting optimal base frames, resulting in improved image quality as shown on synthetic and real datasets.
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.