Deep reverse tone mapping

By Yuki Endo, Yoshihiro Kanamori, Jun Mitani. ACM Transactions on Graphics, 2017

344 citations recorded by the source corpus.

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An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, methodology): “CNNs have been used as the basis of deep learning frameworks in many research works [256]–[259].”
HDR image reconstruction from a single exposure using deep CNNs (2017, background): “Finally, it should be mentioned that the concurrent work by Endo et al. [2017] also treats inverse tone-mapping using deep learning algorithms, by using a different pipeline design.”
Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images (2018, background): “Recently, methods [28], [31] have been proposed to train a CNN network to map the low dynamic range (LDR) images to HDR images.”
Attention-Guided Network for Ghost-Free High Dynamic Range Imaging (2019, methodology): “Endo [4] synthesized multiple LDR images from one LDR image with the deep-learning-based approach, then reconstructed an HDR image by merging them.”
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content (2018, methodology): “Following the Deep Reverse Tone Mapping [EKM17], the same database of CRFs was used [GN03], and the same method of obtaining ﬁve representative CRFs by k-means clustering was adopted.”