While vision transformers show promise in numerous image restoration (IR) tasks, the challenge remains in efficiently generalizing and scaling up a model for multiple IR tasks. To strike a balance between efficiency and model capacity for a generalized transformer-based IR method, we propose a hierarchical information flow mechanism for image restoration, dubbed Hi-IR, which progressively propagates information among pixels in a bottom-up manner. Hi-IR constructs a hierarchical information tree representing the degraded image across three levels. Each level encapsulates different types of information, with higher levels encompassing broader objects and concepts and lower levels focusing on local details. Moreover, the hierarchical tree architecture removes long-range self-attention, improves the computational efficiency and memory utilization, thus preparing it for effective model scaling. Based on that, we explore model scaling to improve our method's capabilities, which is expected to positively impact IR in large-scale training settings. Extensive experimental results show that Hi-IR achieves state-of-the-art performance in seven common image restoration tasks, affirming its effectiveness and generalizability.

本研究针对现有图像恢复方法在效率和适应性方面的不足，提出了一种层次化信息流机制Hi-IR。该方法通过构建三层信息树，逐步传播像素信息，从而在保持模型容量的同时提升计算效率和内存利用率。研究表明，Hi-IR在七种常见图像恢复任务中均取得了最先进的性能，展示了其良好的有效性与推广性。

层次化信息流：用于通用高效图像恢复