Papers › Single image dehazing for a variety of haze scenarios using back projected pyramid network
Single image dehazing for a variety of haze scenarios using back projected pyramid network
Ayush Singh, Ajay Bhave, Dilip K. Prasad
Learning to dehaze single hazy images, especially using a small training dataset is quite challenging. We propose a novel generative adversarial network architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple levels of complexities while retaining spatial context through iterative blocks of UNets and structural information of multiple scales through a novel pyramidal convolution block. These blocks together for the generator and are amenable to learning through back projection. We have shown that our network can be trained without over-fitting using as few as 20 image pairs of hazy and non-hazy images. We report the state of the art performances on NTIRE 2018 homogeneous haze datasets for indoor and outdoor images, NTIRE 2019 denseHaze dataset, and NTIRE 2020 non-homogeneous haze dataset.
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 |
|---|---|---|---|---|---|---|---|
| Image Dehazing | Dense-Haze | BPPNet | PSNR | 17.01 | #3 of 5 | Archive leaderboard | report |
| Image Dehazing | Dense-Haze | BPPNet | SSIM | 0.613 | #3 of 5 | Archive leaderboard | report |
| Image Dehazing | I-Haze | BPPNet | PSNR | 22.56 | #2 of 4 | Archive leaderboard | report |
| Image Dehazing | I-Haze | BPPNet | SSIM | 0.8994 | #2 of 4 | Archive leaderboard | report |
| Image Dehazing | O-Haze | BPPNet | PSNR | 24.27 | #5 of 7 | Archive leaderboard | report |
| Image Dehazing | O-Haze | BPPNet | SSIM | 0.8919 | #5 of 7 | 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.
Methods
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