Image restoration techniques, spanning from the convolution to the transformer paradigm, have demonstrated robust spatial representation capabilities to deliver high-quality performance. However, most of these methods, e.g., convolution and the FFN architecture of transformers, only take implicit advantage of the first-order channel interaction and have yet to fully tap into its potential for high-order modeling. To address this, our study delves into modeling channel-dimension relationships, an…