Low dynamic range (LDR) cameras cannot deal with wide dynamic range inputs, frequently leading to local overexposure issues. We present a learning-based system to reduce these artifacts without resorting to complex acquisition mechanisms like alternating exposures or costly processing that are typical of high dynamic range (HDR) imaging. We propose a transformer-based deep neural network (DNN) to infer the missing HDR details. In an ablation study, we show the importance of using a multiscale DNN and train it with the proper cost function to achieve state-of-the-art quality. To aid the reconstruction of the overexposed areas, our DNN takes a reference frame from the past as an additional input. This leverages the commonly occurring temporal instabilities of autoexposure to our advantage: since well-exposed details in the current frame may be overexposed in the future, we use reinforcement learning to train a reference frame selection DNN that decides whether to adopt the current frame as a future reference. Without resorting to alternating exposures, we obtain therefore a causal, HDR hallucination algorithm with potential application in common video acquisition settings. Our demo video can be found at https://drive.google.com/file/d/1-r12BKImLOYCLUoPzdebnMyNjJ4Rk360/view

该研究论文介绍了一种基于学习的系统，通过使用基于Transformer的深度神经网络来减少低动态范围相机在处理宽动态范围输入时经常出现的局部过曝问题，同时不需要复杂的曝光交替或昂贵的处理来实现高动态范围成像。研究采用了多尺度深度神经网络，并使用合适的代价函数进行训练以达到现有技术水平的质量。通过引入过去的参考帧作为额外输入，帮助重建过曝区域，利用自动曝光中常见的暂时不稳定性，训练了一个决定是否将当前帧作为未来参考帧的参考帧选择深度神经网络。因此，无需交替曝光，得到了一种因果关系的潜在应用于常见视频采集环境的高动态范围幻影算法。

自适应参考帧选择中的在线过曝像素幻象