Papers › Dark and Bright Channel Prior Embedded Network for Dynamic Scene Deblurring
Dark and Bright Channel Prior Embedded Network for Dynamic Scene Deblurring
Jianrui Cai, WangMeng Zuo, and Lei Zhang
Recent years have witnessed the significant progress on convolutional neural networks (CNNs) in dynamic scene deblurring. While most of the CNN models are generally learned by the reconstruction loss defined on training data, incorporating suitable image priors as well as regularization terms into the network architecture could boost the deblurring performance. In this work, we propose a Dark and Bright Channel Priors embedded Network (DBCPeNet) to plug the channel priors into a neural network for effective dynamic scene deblurring. A novel trainable dark and bright channel priors embedded layer (DBCPeL) is developed to aggregate both channel priors and blurry image representations, and a sparse regularization is introduced to regularize the DBCPeNet model learning. Furthermore, we present an effective multi-scale network architecture, namely image full scale exploitation (IFSE), which works in both coarse-to-fine and fine-to-coarse manners for better exploiting information flow across scales. Experimental results on the GoPro and Kohler datasets show that our proposed DBCPeNet performs ¨ favorably against state-of-the-art deep image deblurring methods in terms of both quantitative metrics and visual quality.
Code
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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 | DBCPeNet | PSNR | 31.10 | #46 of 56 | Archive leaderboard | report |
| Deblurring | GoPro | DBCPeNet | SSIM | 0.945 | #46 of 56 | Archive leaderboard | report |
| Image Deblurring | GoPro | DBCPeNet | PSNR | 31.10 | #43 of 55 | Archive leaderboard | report |
| Image Deblurring | GoPro | DBCPeNet | SSIM | 0.945 | #43 of 55 | 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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