  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.
