动态预测曝光时间，提升连拍图像修复质量
Dynamic Exposure Burst Image Restoration
- 根据预览图与运动信息动态预测最优曝光时间
- 在真实相机上实现比现有方法更优的修复效果
- 适合需要高质量连拍修复的手机摄影场景
连拍图像修复旨在从一系列连拍图像中重建高质量图像，这些图像通常使用手动设计的曝光参数拍摄。尽管曝光设置对最终修复效果影响显著，但如何选择最佳曝光参数的问题长期被忽视。本文提出动态曝光连拍图像修复（DEBIR）新框架，通过动态预测适配拍摄环境的曝光时间来提升修复质量。该框架中，连拍自动曝光网络（BAENet）基于预览图像、运动幅度和增益估计每张图像的最优曝光时间；随后，连拍图像修复网络利用这些优化后的曝光图像重建高质量结果。训练方面，我们设计了可微分的连拍模拟器与三阶段训练策略。实验表明，该方法达到当前最优修复性能，并在真实相机系统上验证了其实际可用性。
原文摘要 · Abstract (English)
Burst image restoration aims to reconstruct a high-quality image from burst images, which are typically captured using manually designed exposure settings. Although these exposure settings significantly influence the final restoration performance, the problem of finding optimal exposure settings has been overlooked. In this paper, we present Dynamic Exposure Burst Image Restoration (DEBIR), a novel burst image restoration pipeline that enhances restoration quality by dynamically predicting exposure times tailored to the shooting environment. In our pipeline, Burst Auto-Exposure Network (BAENet) estimates the optimal exposure time for each burst image based on a preview image, as well as motion magnitude and gain. Subsequently, a burst image restoration network reconstructs a high-quality image from burst images captured using these optimal exposure times. For training, we introduce a differentiable burst simulator and a three-stage training strategy. Our experiments demonstrate that our pipeline achieves state-of-the-art restoration quality. Furthermore, we validate the effectiveness of our approach on a real-world camera system, demonstrating its practicality.
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