动态曝光连拍图像复原
Dynamic Exposure Burst Image Restoration
将曝光时间作为可学习变量，通过BAENet根据预览图预测最优曝光，并配合可微分burst模拟器实现端到端训练，为低层视觉任务中传感器参数与算法联合优化提供了新思路。
原文依据 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.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论连拍图像复原旨在从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设置拍摄。尽管这些曝光设置显著影响最终的复原性能，但寻找最优曝光设置的问题一直被忽视。本文提出动态曝光连拍图像复原（DEBIR），一种新颖的连拍图像复原流程，通过动态预测针对拍摄环境定制的曝光时间来提升复原质量。在我们的流程中，连拍自动曝光网络（BAENet）基于预览图像以及运动幅度和增益，为每张连拍图像估计最优曝光时间。随后，连拍图像复原网络从使用这些最优曝光时间拍摄的连拍图像中重建高质量图像。为了训练，我们引入了可微分连拍模拟器和三阶段训练策略。实验表明，我们的流程达到了最优的复原质量。此外，我们在真实相机系统上验证了方法的有效性，证明了其实用性。
英文原文摘要 1174 字符
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.