Thanks to High Dynamic Range (HDR) imaging methods, the scope of photography has seen profound changes recently. To be more specific, such methods try to reconstruct the lost luminosity of the real world caused by the limitation of regular cameras from the Low Dynamic Range (LDR) images. Additionally, although the State-Of-The-Art methods in this topic perform well, they mainly concentrate on combining different exposures and have less attention to extracting the informative parts of the images. Thus, this paper aims to introduce a new model capable of incorporating information from the most visible areas of each image extracted by a visual attention module (VAM), which is a result of a segmentation strategy. In particular, the model, based on a deep learning architecture, utilizes the extracted areas to produce the final HDR image. The results demonstrate that our method outperformed most of the State-Of-The-Art algorithms.

翻译：得益于高动态范围成像方法，摄影领域近期经历了深刻变革。具体而言，此类方法试图从低动态范围图像中重建因常规相机限制而丢失的真实世界亮度信息。此外，尽管当前该领域的先进方法表现优异，但它们主要侧重于不同曝光图像的融合，而较少关注提取图像中的信息区域。为此，本文旨在引入一种新模型，该模型能够整合由视觉注意力模块从每幅图像最显著区域提取的信息——该模块源于一种分割策略。具体地，基于深度学习架构的模型利用提取的区域生成最终的高动态范围图像。结果表明，我们的方法优于大多数现有先进算法。