Papers › MR-VNet: Media Restoration using Volterra Networks
MR-VNet: Media Restoration using Volterra Networks
Siddharth Roheda, Amit Unde, Loay Rashid
This research paper presents a novel class of restoration network architecture based on the Volterra series formulation. By incorporating non-linearity into the system response function through higher order convolutions instead of traditional activation functions we introduce a general framework for image/video restoration. Through extensive experimentation we demonstrate that our proposed architecture achieves state-of-the-art (SOTA) performance in the field of Image/Video Restoration. Moreover we establish that the recently introduced Non-Linear Activation Free Network (NAF-NET) can be considered a special case within the broader class of Volterra Neural Networks. These findings highlight the potential of Volterra Neural Networks as a versatile and powerful tool for addressing complex restoration tasks in computer vision.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Deblurring | GoPro | MR-VNet | PSNR | 34.04 | #8 of 55 | Archive leaderboard | report |
| Image Deblurring | GoPro | MR-VNet | Params (M) | 12.3 | #8 of 55 | Archive leaderboard | report |
| Image Deblurring | GoPro | MR-VNet | SSIM | 0.969 | #8 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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