Global operations, such as global average pooling, are widely used in top-performance image restorers. They aggregate information from input features along entire spatial dimensions but behave differently during training and inference restoration tasks: they based on different regions, namely the cropped patches (from images) full-resolution images. This paper revisits aggregation finds that im...