动态场景下自适应调节快门与感光度，提升HDR成像质量
AdaptiveAE: An Adaptive Exposure Strategy for HDR Capturing in Dynamic Scenes
- 基于强化学习动态优化快门速度与ISO组合
- 在多数据集上实现当前最优的HDR重建效果
- 适合需要高质量动态场景HDR拍摄的应用
主流高动态范围成像技术通常依赖于不同曝光设置（快门速度与ISO）拍摄多张图像进行融合。快门速度与ISO的平衡对高质量HDR成像至关重要：高ISO引入显著噪声，长快门则可能导致明显运动模糊。然而，现有方法常忽略快门速度与ISO之间的复杂交互，且未考虑动态场景中的运动模糊效应。本文提出AdaptiveAE，一种基于强化学习的方法，通过优化快门速度与ISO组合以最大化动态环境下的HDR重建质量。AdaptiveAE整合了包含运动模糊与噪声模拟的图像合成流程，利用语义信息与曝光直方图进行训练，可根据用户定义的曝光时间预算自适应选择最优ISO与快门速度序列，优于传统方案。多个数据集上的实验结果表明其达到当前最优性能。
原文摘要 · Abstract (English)
Mainstream high dynamic range imaging techniques typically rely on fusing multiple images captured with different exposure setups (shutter speed and ISO). A good balance between shutter speed and ISO is crucial for achieving high-quality HDR, as high ISO values introduce significant noise, while long shutter speeds can lead to noticeable motion blur. However, existing methods often overlook the complex interaction between shutter speed and ISO and fail to account for motion blur effects in dynamic scenes. In this work, we propose AdaptiveAE, a reinforcement learning-based method that optimizes the selection of shutter speed and ISO combinations to maximize HDR reconstruction quality in dynamic environments. AdaptiveAE integrates an image synthesis pipeline that incorporates motion blur and noise simulation into our training procedure, leveraging semantic information and exposure histograms. It can adaptively select optimal ISO and shutter speed sequences based on a user-defined exposure time budget, and find a better exposure schedule than traditional solutions. Experimental results across multiple datasets demonstrate that it achieves the state-of-the-art performance.
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