Bracketed High Dynamic Range (HDR) imaging architectures acquire a sequence of Low Dynamic Range (LDR) images in order to either produce a HDR image or an “optimally” exposed LDR image, achieving impressive results under static camera and scene conditions. However, in real world conditions, ghost-like artifacts and noise effects limit the quality of HDR reconstruction. We address these limitations by introducing a post-acquisition snapshot HDR enhancement scheme that generates a bracketed sequence from a small set of LDR images, and in the extreme case, directly from a single exposure. We achieve this goal via a sparse-based approach where transformations between differently exposed images are encoded through a dictionary learning process, while we learn appropriate features by employing a stacked sparse autoencoder (SSAE) based framework. Via experiments with real images, we demonstrate the improved performance of our method over the state-of-the-art, while our single-shot based HDR formulation provides a novel paradigm for the enhancement of LDR imaging and video sequences.

中文翻译：

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通过稀疏表示和特征学习的快照高动态范围成像

包围式高动态范围 (HDR) 成像架构获取一系列低动态范围 (LDR) 图像，以生成 HDR 图像或“最佳”曝光的 LDR 图像，从而在静态相机和场景条件下获得令人印象深刻的效果。然而，在现实世界条件下，类似鬼影的伪影和噪声影响限制了 HDR 重建的质量。我们通过引入采集后快照 HDR 增强方案来解决这些限制，该方案从一小组 LDR 图像生成括号序列，在极端情况下，直接从单次曝光生成括号序列。我们通过基于稀疏的方法实现了这一目标，其中不同曝光图像之间的转换通过字典学习过程进行编码，同时我们通过采用基于堆叠稀疏自动编码器 (SSAE) 的框架来学习适当的特征。