Transformer-based approaches have achieved superior performance in image restoration, since they can model long-term dependencies well. However, the limitation in capturing local information restricts their capacity to remove degradations. While existing approaches attempt to mitigate this issue by incorporating convolutional operations, the core component in Transformer, i.e., self-attention, which serves as a low-pass filter, could unintentionally dilute or even eliminate the acquired local patterns. In this paper, we propose HIT, a simple yet effective High-frequency Injected Transformer for image restoration. Specifically, we design a window-wise injection module (WIM), which incorporates abundant high-frequency details into the feature map, to provide reliable references for restoring high-quality images. We also develop a bidirectional interaction module (BIM) to aggregate features at different scales using a mutually reinforced paradigm, resulting in spatially and contextually improved representations. In addition, we introduce a spatial enhancement unit (SEU) to preserve essential spatial relationships that may be lost due to the computations carried out across channel dimensions in the BIM. Extensive experiments on 6 tasks (real noise, rain streak, blur, flare, underwater conditions, and low-light conditions) demonstrate that HIT with linear computational complexity performs favorably against the state-of-the-art methods. The source code is available at https://github.com/joshyZhou/HIT_.

Effectively leveraging motion information is crucial for the image deblurring task. Existing methods typically build deep-learning models to restore a clean image by estimating blur patterns over the entire movement. This suggests that the blur caused by rotational motion components is processed together with the translational one. Exploring the movement without separation leads to limited performance for complex motion deblurring, especially rotational motion. In this paper, we propose Motion Decomposition Transformer (MDT), a transformer-based architecture augmented with polarized modules for deblurring via motion vector decomposition. MDT consists of a Motion Decomposition Module (MDM) for extracting hybrid rotation and translation features and a Radial Stripe Attention Solver (RSAS) for sharp image reconstruction with enhanced rotational information. Specifically, the MDM uses a deformable Cartesian convolutional branch to capture translational motion, complemented by a polar-system branch to capture rotational motion. The RSAS employs radial stripe windows and angular relative positional encoding in the polar system to enhance rotational information. This design preserves translational details while keeping computational costs lower than dual-coordinate design. Experimental results on 6 image deblurring datasets show that MDT outperforms state-of-the-art methods, particularly in handling blur caused by complex motions with significant rotational components. The code and pre-trained models are available at https://github.com/Calvin11311/MDT.

Transformer-based approaches have gained significant attention in image restoration, where the core component, i.e, Multi-Head Attention (MHA), plays a crucial role in capturing diverse features and recovering high-quality results. In MHA, heads perform attention calculation independently from uniform split subspaces, and a redundancy issue is triggered to hinder the model from achieving satisfactory outputs. In this paper, we propose to improve MHA by exploring diverse learners and introducing various interactions between heads, which results in a Hierarchical multI-head atteNtion driven Transformer model, termed HINT, for image restoration. HINT contains two modules, i.e., the Hierarchical Multi-Head Attention (HMHA) and the Query-Key Cache Updating (QKCU) module, to address the redundancy problem that is rooted in vanilla MHA. Specifically, HMHA extracts diverse contextual features by employing heads to learn from subspaces of varying sizes and containing different information. Moreover, QKCU, comprising intra- and inter-layer schemes, further reduces the redundancy problem by facilitating enhanced interactions between attention heads within and across layers. Extensive experiments are conducted on 12 benchmarks across 5 image restoration tasks, including low-light enhancement, dehazing, desnowing, denoising, and deraining, to demonstrate the superiority of HINT. The source code is available in the supplementary materials.

Lens flare occurs when shooting towards strong light sources, significantly degrading the visual quality of images. Due to the difficulty in capturing flare-corrupted and flare-free image pairs in the real world, existing datasets are typically synthesized in 2D by overlaying artificial flare templates onto background images. However, the lack of flare diversity in templates and the neglect of physical principles in the synthesis process hinder models trained on these datasets from generalizing well to real-world scenarios. To address these challenges, we propose a new physics-informed method for flare data generation, which consists of three stages: parameterized template creation, the laws of illumination-aware 2D synthesis, and physical engine-based 3D rendering, which finally gives us a mixed flare dataset that incorporates both 2D and 3D perspectives, namely FlareX. This dataset offers 9,500 2D templates derived from 95 flare patterns and 3,000 flare image pairs rendered from 60 3D scenes. Furthermore, we design a masking approach to obtain real-world flare-free images from their corrupted counterparts to measure the performance of the model on real-world images. Extensive experiments demonstrate the effectiveness of our method and dataset.

Flicker artifacts in short-exposure images are caused by the interplay between the row-wise exposure mechanism of rolling shutter cameras and the temporal intensity variations of alternating current (AC)-powered lighting. These artifacts typically appear as uneven brightness distribution across the image, forming noticeable dark bands. Beyond compromising image quality, this structured noise also affects high-level tasks, such as object detection and tracking, where reliable lighting is crucial. Despite the prevalence of flicker, the lack of a large-scale, realistic dataset has been a significant barrier to advancing research in flicker removal. To address this issue, we present BurstDeflicker, a scalable benchmark constructed using three complementary data acquisition strategies. First, we develop a Retinex-based synthesis pipeline that redefines the goal of flicker removal and enables controllable manipulation of key flicker-related attributes (e.g., intensity, area, and frequency), thereby facilitating the generation of diverse flicker patterns. Second, we capture 4,000 real-world flicker images from different scenes, which help the model better understand the spatial and temporal characteristics of real flicker artifacts and generalize more effectively to wild scenarios. Finally, due to the non-repeatable nature of dynamic scenes, we propose a green-screen method to incorporate motion into image pairs while preserving real flicker degradation. Comprehensive experiments demonstrate the effectiveness of our dataset and its potential to advance research in flicker removal.

Transformer-based approaches have shown promising performance in image restoration tasks due to their ability to model long-range dependencies, which are essential for recovering clear images. Although various efficient attention mechanisms have been proposed to address the intensive computational loads of transformers, they often suffer from redundant information and noisy interactions from irrelevant regions, as they consider all available tokens. In this work, we propose an Adaptive Sparse Transformer (AST-v2) to mitigate these issues by reducing noisy interactions in irrelevant areas and removing feature redundancy along channel dimension. AST-v2 incorporates two core components: an Adaptive Sparse Self-Attention (ASSA) block and a Feature Refinement Feed-forward Network (FRFN). ASSA adopts a dual-branch design, where the sparse branch guides the modulation of standard dense attention weights. This paradigm reduces the negative impact of irrelevant token interactions while preserving the important ones. Meanwhile, FRFN utilizes an enhance-and-ease scheme to eliminate feature redundancy across channels, enhancing the restoration of clear images. Experimental results on commonly used benchmarks show the competitive performance of our method for 6 restoration tasks, including rain streak removal, haze removal, shadow removal, snow removal, blur removal, and low-light enhancement. The code is available in the supplementary materials.

Transformer-based approaches have achieved promising performance in image restoration tasks, given their ability to model long-range dependencies, which is crucial for recovering clear images. Though diverse efficient attention mechanism designs have addressed the intensive computations associated with using transformers, they often involve redundant information and noisy interactions from irrelevant regions by considering all available tokens. In this work, we propose an Adaptive Sparse Transformer (AST) to mitigate the noisy interactions of irrelevant areas and remove feature redundancy in both spatial and channel domains. AST comprises two core designs, i.e., an Adaptive Sparse Self-Attention (ASSA) block and a Feature Refinement Feed-forward Network (FRFN). Specifically, ASSA is adaptively computed using a two-branch paradigm, where the sparse branch is introduced to filter out the negative impacts of low query-key matching scores for aggregating features, while the dense one ensures sufficient information flow through the network for learning discriminative representations. Meanwhile, FRFN employs an enhance-and-ease scheme to eliminate feature redundancy in channels, enhancing the restoration of clear latent images. Experimental results on commonly used benchmarks have demonstrated the versatility and competitive performance of our method in several tasks, including rain streak removal, real haze removal, and raindrop removal. The code and pre-trained models are available at https://github.com/joshyZhou/AST.

Recent studies have proven the strength of task-oriented point cloud sampling methods over traditional non-learned ones. However, previous task-oriented samplers are not adequate to extract local details and spatial patterns of point clouds, limiting the quality of synthesized points. In this paper, we present a classification-oriented sampling network named CO-Net, aiming to learn informative sampled points that benefit downstream classification tasks. Specifically, we propose Attentive Point Spatial Pyramid (APSP) to achieve attention-based aggregation of multi-scale contexts. Then, we develop Gradient-weighted Feature Enhancement (GFE) to highlight class-discriminative structural details. We further design Non-overlapped Local Adjustment (NLA) to ease noise impacts with neighborhood contexts while preserving distinctive features of central points for more informative results. Experiments demonstrate that CO-Net outperforms state-of-the-art sampling methods.

In this paper, we aim to reconstruct a full 3D human shape from a single image. Previous vertex-level and parameter regression approaches reconstruct 3D human shape based on a pre-defined adjacency matrix to encode positive relations between nodes. The deep topological relations for the surface of the 3D human body are not carefully exploited. Moreover, the performance of most existing approaches often suffer from domain gap when handling more occlusion cases in real-world scenes. In this work, we propose a Deep Mesh Relation Capturing Graph Convolution Network, DC-GNet, with a shape completion task for 3D human shape reconstruction. Firstly, we propose to capture deep relations within mesh vertices, where an adaptive matrix encoding both positive and negative relations is introduced. Secondly, we propose a shape completion task to learn prior about various kinds of occlusion cases. Our approach encodes mesh structure from more subtle relations between nodes in a more distant region. Furthermore, our shape completion module alleviates the performance degradation issue in the outdoor scene. Extensive experiments on several benchmarks show that our approach outperforms the previous 3D human pose and shape estimation approaches.