  Traditional single-task image restoration methods excel in handling specific
degradation types but struggle with multiple degradations. To address this
limitation, we propose Grouped Restoration with Image Degradation Similarity
(GRIDS), a novel approach that harmonizes the competing objectives inherent in
multiple-degradation restoration. We first introduce a quantitative method for
assessing relationships between image degradations using statistical modeling
of deep degradation representations. This analysis facilitates the strategic
grouping of similar tasks, enhancing both the efficiency and effectiveness of
the restoration process. Based on the degradation similarity, GRIDS divides
restoration tasks into one of the optimal groups, where tasks within the same
group are highly correlated. For instance, GRIDS effectively groups 11
degradation types into 4 cohesive groups. Trained models within each group show
significant improvements, with an average improvement of 0.09dB over
single-task upper bound models and 2.24dB over the mix-training baseline model.
GRIDS incorporates an adaptive model selection mechanism for inference,
automatically selecting the appropriate grouped-training model based on the
input degradation. This mechanism is particularly useful for real-world
scenarios with unknown degradations as it does not rely on explicit degradation
classification modules. Furthermore, our method can predict model
generalization ability without the need for network inference, providing
valuable insights for practitioners.
