Papers › Learning Continuous Exposure Value Representations for Single-Image HDR Reconstruction
Learning Continuous Exposure Value Representations for Single-Image HDR Reconstruction
Su-Kai Chen, Hung-Lin Yen, Yu-Lun Liu, Min-Hung Chen, Hou-Ning Hu, Wen-Hsiao Peng, Yen-Yu Lin
Deep learning is commonly used to reconstruct HDR images from LDR images. LDR stack-based methods are used for single-image HDR reconstruction, generating an HDR image from a deep learning-generated LDR stack. However, current methods generate the stack with predetermined exposure values (EVs), which may limit the quality of HDR reconstruction. To address this, we propose the continuous exposure value representation (CEVR), which uses an implicit function to generate LDR images with arbitrary EVs, including those unseen during training. Our approach generates a continuous stack with more images containing diverse EVs, significantly improving HDR reconstruction. We use a cycle training strategy to supervise the model in generating continuous EV LDR images without corresponding ground truths. Our CEVR model outperforms existing methods, as demonstrated by experimental results.
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Code
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Tasks
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 | CEVR | HDR-VDP-2 | 59.00 | #1 of 9 | Archive leaderboard | report |
| inverse tone mapping | VDS dataset: Multi exposure stack-based inverse tone mapping | CEVR | Kim and Kautz TMO-PSNR | 30.04 | #1 of 9 | Archive leaderboard | report |
| inverse tone mapping | VDS dataset: Multi exposure stack-based inverse tone mapping | CEVR | Reinhard'TMO-PSNR | 34.67 | #1 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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