High dynamic range (HDR) imaging techniques have witnessed a great improvement in the past few decades. However, saliency detection task on HDR content is still far from well explored. In this paper, we introduce a multi-exposure decomposition-fusion model for HDR image saliency detection inspired by the brightness adaption mechanism. The proposed model is composed of three modules. Firstly, a decomposition module converts the input raw HDR image into a stack of LDR images by uniformly sampling the exposure time range. Secondly, a saliency region proposal network is employed to generate the candidate saliency maps for each LDR image in the exposure stack. Finally, an uncertainty weighting based fusion algorithm is applied to generate the overall saliency map for the input HDR image by merging the obtained LDR saliency maps. Extensive experiments show that our proposed model achieves superior performance compared with the state-of-the-art methods on the existing HDR eye fixation databases. The source code of the proposed model are made publicly available at https://github.com/sunnycia/DFHSal.

中文翻译：

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用于高动态范围图像显着性检测的多曝光分解融合模型

在过去的几十年中，高动态范围 (HDR) 成像技术取得了巨大进步。然而，HDR 内容的显着性检测任务还远未得到很好的探索。在本文中，我们介绍了一种受亮度自适应机制启发的多曝光分解融合模型，用于 HDR 图像显着性检测。所提出的模型由三个模块组成。首先，分解模块通过对曝光时间范围进行均匀采样，将输入的原始 HDR 图像转换为一堆 LDR 图像。其次，采用显着区域提议网络为曝光堆栈中的每个 LDR 图像生成候选显着图。最后，应用基于不确定性加权的融合算法，通过合并获得的 LDR 显着图来生成输入 HDR 图像的整体显着图。大量实验表明，与现有 HDR 眼睛注视数据库上的最新方法相比，我们提出的模型实现了卓越的性能。所提议模型的源代码可在 https://github.com/sunnycia/DFHSal 上公开获得。