Papers › Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
Jingwen He, Chao Dong, Yu Qiao
In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods. Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters. We make a step forward by proposing a unified CNN framework that consists of few additional parameters than a single-level model yet could handle arbitrary restoration levels between a start and an end level. The additional module, namely AdaFM layer, performs channel-wise feature modification, and can adapt a model to another restoration level with high accuracy. By simply tweaking an interpolation coefficient, the intermediate model - AdaFM-Net could generate smooth and continuous restoration effects without artifacts. Extensive experiments on three image restoration tasks demonstrate the effectiveness of both model training and modulation testing. Besides, we carefully investigate the properties of AdaFM layers, providing a detailed guidance on the usage of the proposed method.
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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 |
|---|---|---|---|---|---|---|---|
| Color Image Denoising | CBSD68 sigma15 | AdaFM-Net | PSNR | 34.1 | #5 of 10 | Archive leaderboard | report |
| Color Image Denoising | CBSD68 sigma75 | AdaFM-Net | PSNR | 26.35 | #2 of 4 | Archive leaderboard | report |
| Image Super-Resolution | Set5 - 3x upscaling | AdaFM-Net | PSNR | 34.34 | #23 of 32 | 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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