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：高动态范围（HDR）成像仍是现代数码摄影中的一项挑战性任务。近年研究提出的解决方案虽能提供高质量的图像采集，却以极其庞大的运算量和缓慢的推理速度为代价，阻碍了这些方案在轻量级实时系统上的部署。本文提出CEN-HDR，一种基于轻量注意力机制与亚像素卷积运算的新型架构，实现了面向实时HDR成像的高效计算神经网络。同时，我们通过知识蒸馏应用网络压缩技术，提出了一种高效的训练方案。经过大量定性与定量比较，结果表明我们的方法在图像质量上具有竞争力，同时速度优于现有最先进解决方案，使其能够在实时约束条件下实际部署。实验结果显示，在Macbook M1 NPU上，我们的方法在Kalantari2017数据集上的mu-PSNR得分为43.04，帧率可达33 FPS。