Window-based Transformers have substantially improved the practicality of image restoration techniques by reducing computational costs, but their local attention mechanism introduces a bottleneck that limits further performance gains. Expanding the attention range holds promise for better image restoration quality. However, the expansion faces two fundamental issues: (1) prohibitive quadratic growth in computational complexity, and (2) increased attention noise in feature aggregation caused by the inclusion of more irrelevant pixels. In this paper, we propose RouteWinFormer, a novel window-based Transformer that enables wider-range attention in image restoration. RouteWinFormer incorporates the Route-Window Attention Module (RWAM) to efficiently extend attention to mid-range regions, which are identified as critical for image restoration according to an analysis of normalized average attention distances. RWAM adopts a router that dynamically selects a subset of nearby windows for aggregation, rather than including all neighboring windows, alleviating the growth in computational complexity. The selection leverages regional similarity based on a window-level description, reducing the inclusion of irrelevant pixels and attention noise during aggregation. In addition, we introduce Multi-Scale Structure Regularization (MSR) for RWAM to learn a better structure prior. MSR encourages the sub-scale branches of a U-shaped network to extract structural information, which serves as a condition for the original-scale branch to learn degradation patterns. Experimental results show that RouteWinFormer outperforms state-of-the-art methods on 8 benchmarks across diverse image restoration tasks, including defocus deblurring, desnowing, dehazing, deraining, and motion deblurring. The source code is available at: https://github.com/QifanLi-vilab/RouteWinFormer.

The common occurrence of occlusion-induced incompleteness in point clouds has made point cloud completion (PCC) a highly-concerned task in the field of geometric processing. Existing PCC methods typically produce complete point clouds from partial point clouds in a coarse-to-fine paradigm, with the coarse stage generating entire shapes and the fine stage improving texture details. Though diffusion models have demonstrated effectiveness in the coarse stage, the fine stage still faces challenges in producing high-fidelity results due to the ill-posed nature of PCC. The intrinsic contextual information for texture details in partial point clouds is the key to solving the challenge. In this paper, we propose a high-fidelity PCC method that digs into both short and long-range contextual information from the partial point cloud in the fine stage. Specifically, after generating the coarse point cloud via a diffusion-based coarse generator, a mixed sampling module introduces short-range contextual information from partial point clouds into the fine stage. A surface freezing module safeguards points from noise-free partial point clouds against disruption. As for the long-range contextual information, we design a similarity modeling module to derive similarity with rigid transformation invariance between points, conducting effective matching of geometric manifold features globally. In this way, the high-quality components present in the partial point cloud serve as valuable references to refine the coarse point cloud with high fidelity. Extensive experiments have demonstrated the superiority of the proposed method over SOTA competitors.