UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity
This work addresses the challenge of universal image restoration for computer vision applications, offering an incremental improvement over existing all-in-one methods.
The paper tackles the problem of all-in-one image restoration by proposing UniRestorer, which uses hierarchical clustering and a mixture-of-experts model to adaptively estimate degradation at proper granularity, resulting in improved performance that outperforms state-of-the-art methods by a large margin and narrows the gap to single-task models.
Recently, considerable progress has been made in all-in-one image restoration. Generally, existing methods can be degradation-agnostic or degradation-aware. However, the former are limited in leveraging degradation-specific restoration, and the latter suffer from the inevitable error in degradation estimation. Consequently, the performance of existing methods has a large gap compared to specific single-task models. In this work, we make a step forward in this topic, and present our UniRestorer with improved restoration performance. Specifically, we perform hierarchical clustering on degradation space, and train a multi-granularity mixture-of-experts (MoE) restoration model. Then, UniRestorer adopts both degradation and granularity estimation to adaptively select an appropriate expert for image restoration. In contrast to existing degradation-agnostic and -aware methods, UniRestorer can leverage degradation estimation to benefit degradation specific restoration, and use granularity estimation to make the model robust to degradation estimation error. Experimental results show that our UniRestorer outperforms state-of-the-art all-in-one methods by a large margin, and is promising in closing the performance gap to specific single task models.