DSwinIR: Rethinking Window-based Attention for Image Restoration

Image restoration has witnessed significant advancements with the development of deep learning models. Especially Transformer-based models, particularly those leveraging window-based self-attention, have become a dominant force in image restoration. However, their performance is fundamentally constrained by the rigid, non-overlapping window partitioning scheme, which leads to two critical limitations: insufficient feature interaction across window boundaries and content-agnostic receptive fields that cannot adapt to diverse image structures. Existing methods often rely on heuristic patterns to mitigate these issues, rather than addressing the root cause. In this paper, we propose the Deformable Sliding Window Transformer (DSwinIR), a new foundational backbone architecture that systematically overcomes these limitations. At the heart of DSwinIR is the proposed novel Deformable Sliding Window (DSwin) Attention. This mechanism introduces two fundamental innovations. First, it replaces the rigid partitioning with a token-centric sliding window paradigm, ensuring seamless cross-window information flow and effectively eliminating boundary artifacts. Second, it incorporates a content-aware deformable sampling strategy, which allows the attention mechanism to learn data-dependent offsets and dynamically shape its receptive fields to focus on the most informative image regions. This synthesis endows the model with both strong locality-aware inductive biases and powerful, adaptive long-range modeling capabilities. Extensive experiments show that DSwinIR sets a new state-of-the-art across a wide spectrum of image restoration tasks. For instance, in all-in-one restoration, our DSwinIR surpasses the most recent backbone GridFormer by over 0.53 dB on the three-task benchmark and a remarkable 0.86 dB on the five-task benchmark.