High dynamic range (HDR) imaging is still a challenging task in modern digital photography. Recent research proposes solutions that provide high-quality acquisition but at the cost of a very large number of operations and a slow inference time that prevent the implementation of these solutions on lightweight real-time systems. In this paper, we propose CEN-HDR, a new computationally efficient neural network by providing a novel architecture based on a light attention mechanism and sub-pixel convolution operations for real-time HDR imaging. We also provide an efficient training scheme by applying network compression using knowledge distillation. We performed extensive qualitative and quantitative comparisons to show that our approach produces competitive results in image quality while being faster than state-of-the-art solutions, allowing it to be practically deployed under real-time constraints. Experimental results show our method obtains a score of 43.04 mu-PSNR on the Kalantari2017 dataset with a framerate of 33 FPS using a Macbook M1 NPU.

翻译：在现代数字摄影中,高动态射程成像(HDR)仍然是一项具有挑战性的任务。最近的研究提出了提供高质量获取但以大量操作和缓慢的推论时间为代价的解决方案,这些解决方案阻碍在轻量实时系统上实施这些解决方案。在本文中,我们提议CEN-HDR,这是一个新的计算效率高的神经网络,为实时的HDHR成像提供基于光关注机制和次像级连锁操作的新结构。我们还通过利用知识蒸馏应用网络压缩提供了高效的培训计划。我们进行了广泛的定性和定量比较,以表明我们的方法在图像质量上产生竞争性结果,同时比最先进的解决方案更快,使其在实时限制下实际部署。实验结果表明,我们的方法在Kalantari2017数据集上获得了43.04 m-PSNR的分数,使用Macbook M1 NPU,有33 FPSPS框架。