Any Image Restoration via Efficient Spatial-Frequency Degradation Adaptation

Understand

Restoring multiple degradations efficiently via just one model has become increasingly significant and impactful, especially with the proliferation of mobile devices.

• Traditional solutions typically involve training dedicated models per degradation, resulting in inefficiency and redundancy.
• More recent approaches either introduce additional modules to learn visual prompts, significantly increasing the size of the model, or incorporate cross modal transfer from large language models trained on vast datasets, adding complexity to the system architecture.
• In contrast, our approach, termed AnyIR, takes a unified path that leverages inherent similarity across various degradations to enable both efficient and comprehensive restoration through a joint embedding mechanism, without scaling up the model or relying on large language models.