DSwinIR: 重新思考图像修复中的基于窗口的注意力
DSwinIR: Rethinking Window-based Attention for Image Restoration
AI总结：
DSwinIR提出了一种新的可变形滑动窗口注意力机制，通过token为中心和内容感知的方法提升图像修复性能，实现更灵活的特征交互和感受野调整。
摘要：
图像修复随着深度学习模型的发展取得了显著进展。基于变换器的模型，特别是使用基于窗口的自注意力机制的模型，已成为主流。然而，它们的性能受到刚性、非重叠窗口分区方案的限制，导致窗口之间特征交互不足和感受野有限。这突显了需要更适应性和灵活的注意力机制的必要性。在本文中，我们提出了用于图像修复的可变形滑动窗口变换器（DSwinIR），一种新的注意力机制：可变形滑动窗口（DSwin）注意力。该机制引入了一种以token为中心和内容感知的范式，超越了网格和固定窗口分区。它包含两个互补的组成部分。首先，它用一种以token为中心的滑动窗口范式替代了刚性分区，使其能够有效消除边界伪影。其次，它结合了一种内容感知的可变形采样策略，使注意力机制能够学习数据依赖的偏移量，并主动塑造其感受野，以聚焦于最具有信息量的图像区域。广泛的实验表明，DSwinIR在多个评估基准上实现了强大的结果，包括在所有任务图像修复中，我们的DSwinIR在三个任务基准上超越了最新的骨干网络GridFormer 0.53 dB，在五个任务基准上超越了0.87 dB。
英文摘要：
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. In this paper, we propose the Deformable Sliding Window Transformer for Image Restoration (DSwinIR), a new attention mechanism: the {Deformable Sliding Window (DSwin) Attention}. {This mechanism introduces a token-centric and content-aware paradigm that moves beyond the grid and fixed window partition.} It comprises two complementary components. First, it replaces the rigid partitioning with a \textit{token-centric sliding window} paradigm, {making it effective at eliminating boundary artifacts}. Second, it incorporates a \textit{content-aware deformable sampling} strategy, which allows the attention mechanism to learn data-dependent offsets and actively shape its receptive field to focus on the most informative image regions. Extensive experiments show that DSwinIR achieves strong results, including state-of-the-art performance on several evaluated benchmarks. For instance, in all-in-one image restoration, our DSwinIR surpasses the most recent backbone GridFormer by 0.53 dB on the three-task benchmark and 0.87 dB on the five-task benchmark.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
