Papers › Neural Image Re-Exposure
Neural Image Re-Exposure
Xinyu Zhang, Hefei Huang, Xu Jia, Dong Wang, Huchuan Lu
The shutter strategy applied to the photo-shooting process has a significant influence on the quality of the captured photograph. An improper shutter may lead to a blurry image, video discontinuity, or rolling shutter artifact. Existing works try to provide an independent solution for each issue. In this work, we aim to re-expose the captured photo in post-processing to provide a more flexible way of addressing those issues within a unified framework. Specifically, we propose a neural network-based image re-exposure framework. It consists of an encoder for visual latent space construction, a re-exposure module for aggregating information to neural film with a desired shutter strategy, and a decoder for 'developing' neural film into a desired image. To compensate for information confusion and missing frames, event streams, which can capture almost continuous brightness changes, are leveraged in computing visual latent content. Both self-attention layers and cross-attention layers are employed in the re-exposure module to promote interaction between neural film and visual latent content and information aggregation to neural film. The proposed unified image re-exposure framework is evaluated on several shutter-related image recovery tasks and performs favorably against independent state-of-the-art methods.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Deblurring | GoPro | NIRE | PSNR | 35.03 | #5 of 56 | Archive leaderboard | report |
| Deblurring | GoPro | NIRE | SSIM | 0.973 | #5 of 56 | Archive leaderboard | report |
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