Papers › Deep Recursive HDRI: Inverse Tone Mapping using Generative Adversarial Networks
Deep Recursive HDRI: Inverse Tone Mapping using Generative Adversarial Networks
Siyeong Lee, Gwon Hwan An, Suk-Ju Kang
High dynamic range images contain luminance information of the physical world and provide more realistic experience than conventional low dynamic range images. Because most images have a low dynamic range, recovering the lost dynamic range from a single low dynamic range image is still prevalent. We propose a novel method for restoring the lost dynamic range from a single low dynamic range image through a deep neural network. The proposed method is the first framework to create high dynamic range images based on the estimated multi-exposure stack using the conditional generative adversarial network structure. In this architecture, we train the network by setting an objective function that is a combination of L1 loss and generative adversarial network loss. In addition, this architecture has a simplified structure than the existing networks. In the experimental results, the proposed network generated a multi-exposure stack consisting of realistic images with varying exposure values while avoiding artifacts on public benchmarks, compared with the existing methods. In addition, both the multi-exposure stacks and high dynamic range images estimated by the proposed method are significantly similar to the ground truth than other state-of-the-art algorithms.
Code
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| inverse tone mapping | VDS dataset: Multi exposure stack-based inverse tone mapping | Deep Recursive HDRI | HDR-VDP-2 | 57.28 | #3 of 9 | Archive leaderboard | report |
| inverse tone mapping | VDS dataset: Multi exposure stack-based inverse tone mapping | Deep Recursive HDRI | HDR-VDP-3 | 8.48 | #3 of 9 | Archive leaderboard | report |
| inverse tone mapping | VDS dataset: Multi exposure stack-based inverse tone mapping | Deep Recursive HDRI | Kim and Kautz TMO-PSNR | 28.02 | #3 of 9 | Archive leaderboard | report |
| inverse tone mapping | VDS dataset: Multi exposure stack-based inverse tone mapping | Deep Recursive HDRI | PU21-PSNR | 25.88 | #3 of 9 | Archive leaderboard | report |
| inverse tone mapping | VDS dataset: Multi exposure stack-based inverse tone mapping | Deep Recursive HDRI | PU21-SSIM | 0.8874 | #3 of 9 | Archive leaderboard | report |
| inverse tone mapping | VDS dataset: Multi exposure stack-based inverse tone mapping | Deep Recursive HDRI | Reinhard'TMO-PSNR | 32.94 | #3 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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