Fusing low dynamic range (LDR) for high dynamic range (HDR) images has gained a lot of attention, especially to achieve real-world application significance when the hardware resources are limited to capture images with different exposure times. However, existing HDR image generation by picking the best parts from each LDR image often yields unsatisfactory results due to either the lack of input images or well-exposed contents. To overcome this limitation, we model the HDR image generation process in two-exposure fusion as a deep reinforcement learning problem and learn an online compensating representation to fuse with LDR inputs for HDR image generation. Moreover, we build a two-exposure dataset with reference HDR images from a public multiexposure dataset that has not yet been normalized to train and evaluate the proposed model. By assessing the built dataset, we show that our reinforcement HDR image generation significantly outperforms other competing methods under different challenging scenarios, even with limited well-exposed contents. More experimental results on a no-reference multiexposure image dataset demonstrate the generality and effectiveness of the proposed model. To the best of our knowledge, this is the first work to use a reinforcement-learning-based framework for an online compensating representation in two-exposure image fusion.

中文翻译：

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具有深度强化学习的自动中间生成，用于鲁棒的两次曝光图像融合


将低动态范围（LDR）融合为高动态范围（HDR）图像已引起广泛关注，特别是当硬件资源有限以捕获不同曝光时间的图像时，实现现实世界的应用意义。然而，由于缺乏输入图像或曝光良好的内容，现有的通过从每个 LDR 图像中挑选最佳部分来生成 HDR 图像的方法通常会产生不令人满意的结果。为了克服这一限制，我们将两次曝光融合中的 HDR 图像生成过程建模为深度强化学习问题，并学习在线补偿表示以与 LDR 输入融合以生成 HDR 图像。此外，我们使用来自公共多重曝光数据集的参考 HDR 图像构建了一个两次曝光数据集，该数据集尚未标准化，以训练和评估所提出的模型。通过评估构建的数据集，我们表明，即使在曝光良好的内容有限的情况下，我们的强化 HDR 图像生成在不同的挑战性场景下也显着优于其他竞争方法。在无参考多重曝光图像数据集上的更多实验结果证明了所提出模型的通用性和有效性。据我们所知，这是第一个使用基于强化学习的框架在两次曝光图像融合中进行在线补偿表示的工作。