Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image Restoration

Understand

All-in-one image restoration tackles different types of degradations with a unified model instead of having task-specific, non-generic models for each degradation.

• The requirement to tackle multiple degradations using the same model can lead to high-complexity designs with fixed configuration that lack the adaptability to more efficient alternatives.
• We propose DyNet, a dynamic family of networks designed in an encoder-decoder style for all-in-one image restoration tasks.
• Our DyNet can seamlessly switch between its bulkier and lightweight variants, thereby offering flexibility for efficient model deployment with a single round of training.