DSwinIR: Rethinking Window-based Attention for Image Restoration

Understand

Image restoration has witnessed significant advancements with the development of deep learning models.

• Transformer-based models, particularly those using window-based self-attention, have become a dominant force.
• However, their performance is constrained by the rigid, non-overlapping window partitioning scheme, which leads to \textit{insufficient feature interaction across windows and limited receptive fields}.
• This highlights the need for more adaptive and flexible attention mechanisms.