Recently, Deep Learning-based methods for inverse tone mapping standard dynamic range (SDR) images to obtain high dynamic range (HDR) images have become very popular. These methods manage to fill over-exposed areas convincingly both in terms of details and dynamic range. To be effective, deep learning-based methods need to learn from large datasets and transfer this knowledge to the network weights. In this work, we tackle this problem from a completely different perspective. What can we learn from a single SDR 8-bit video? With the presented self-supervised approach, we show that, in many cases, a single SDR video is sufficient to generate an HDR video of the same quality or better than other state-of-the-art methods.

最近，基于深度学习的逆色调映射标准动态范围（SDR）图像以获得高动态范围（HDR）图像的方法变得非常流行。这些方法在细节和动态范围方面都能够令人信服地填充过度曝光的区域。为了有效，基于深度学习的方法需要从大型数据集中学习并将这些知识转移到网络权重。在这项工作中，我们从完全不同的角度解决这个问题。我们可以从单个 SDR 8 位视频中了解到什么？通过所提出的自监督方法，我们表明，在许多情况下，单个 SDR 视频足以生成与其他最先进方法相同或更好质量的 HDR 视频。