Pohang University of Science and Technology (POSTECH)
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.
In the burst sequence, from left to right, the exposure time increases, leading to reduced noise and increased blur.
FSN first constructs the image feature of each frame using CNN.
The constructed image feature is fed into our Correlation-based Motion Aware (CMA) blocks, which update the image feature based on motion information extracted from the burst frames.
To extract motion information from the burst frames, we propose Feature Correlation Module (FCM), which computes the local correlation along both spatial and temporal axes.
BIPNet, when combined with our frame selector, improves the clarity of high-frequency image details by effectively merging complementary information from multiple frames into the chosen base frame.
Conversely, without the frame selector, BIPNet tends to produce blurry images due to significant degradation in the initial frame.
@inproceedings{kim2024burst,
title={Burst Image Super-Resolution with Base Frame Selection},
author={Kim, Sanghyun and Lee, Minjung and Kim, Woohyeok and Jung, Deunsol and Rim, Jaesung and Cho, Sunghyun and Cho, Minsu},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={5940--5949},
year={2024}
}