Papers › Hypernetwork-Based Adaptive Image Restoration

Hypernetwork-Based Adaptive Image Restoration

13 Jun 2022arXiv:2206.05970archive 2025-07-28

Shai Aharon, Gil Ben-Artzi

Adaptive image restoration models can restore images with different degradation levels at inference time without the need to retrain the model. We present an approach that is highly accurate and allows a significant reduction in the number of parameters. In contrast to existing methods, our approach can restore images using a single fixed-size model, regardless of the number of degradation levels. On popular datasets, our approach yields state-of-the-art results in terms of size and accuracy for a variety of image restoration tasks, including denoising, deJPEG, and super-resolution.

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