Cross Aggregation Transformer for Image Restoration

Understand

Recently, Transformer architecture has been introduced into image restoration to replace convolution neural network (CNN) with surprising results.

• Considering the high computational complexity of Transformer with global attention, some methods use the local square window to limit the scope of self-attention.
• However, these methods lack direct interaction among different windows, which limits the establishment of long-range dependencies.
• To address the above issue, we propose a new image restoration model, Cross Aggregation Transformer (CAT).