High dynamic range (HDR) image is widely-used in graphics and photography due to the rich information it contains. Recently the community has started using deep neural network (DNN) to reconstruct standard dynamic range (SDR) images into HDR. Albeit the superiority of current DNN-based methods, their application scenario is still limited: (1) heavy model impedes real-time processing, and (2) inapplicable to legacy SDR content with more degradation types. Therefore, we propose a lightweight DNN-based method trained to tackle legacy SDR. For better design, we reform the problem modeling and emphasize degradation model. Experiments show that our method reached appealing performance with minimal computational cost compared with others.

翻译：高动态范围(HDR)图像由于包含丰富的信息而在图形和摄影中广泛使用。最近,社区开始使用深神经网络(DNN)将标准动态范围(SDR)图像重建为《人类发展报告》。 尽管目前的DNN方法具有优越性,但其应用情景仍然有限:(1) 重模型阻碍实时处理,(2) 不适用于具有更多降解型的遗留特别提款权内容。因此,我们提议了一种以轻量级DNN为基础的方法,用于处理遗留的特别提款权。为了更好地设计,我们改革问题模型,强调退化模型。实验表明,我们的方法与其它方法相比,以最低的计算成本达到了吸引力性能。