Deep generative models have gained considerable attention in low-level vision tasks due to their powerful generative capabilities. Among these, diffusion model-based approaches, which employ a forward diffusion process to degrade an image and a reverse denoising process for image generation, have become particularly prominent for producing high-quality, diverse samples with intricate texture details. Despite their widespread success in low-level vision, there remains a lack of a comprehensive, insightful survey that synthesizes and organizes the advances in diffusion model-based techniques. To address this gap, this paper presents the first comprehensive review focused on denoising diffusion models applied to low-level vision tasks, covering both theoretical and practical contributions. We outline three general diffusion modeling frameworks and explore their connections with other popular deep generative models, establishing a solid theoretical foundation for subsequent analysis. We then categorize diffusion models used in low-level vision tasks from multiple perspectives, considering both the underlying framework and the target application. Beyond natural image processing, we also summarize diffusion models applied to other low-level vision domains, including medical imaging, remote sensing, and video processing. Additionally, we provide an overview of widely used benchmarks and evaluation metrics in low-level vision tasks. Our review includes an extensive evaluation of diffusion model-based techniques across six representative tasks, with both quantitative and qualitative analysis. Finally, we highlight the limitations of current diffusion models and propose four promising directions for future research. This comprehensive review aims to foster a deeper understanding of the role of denoising diffusion models in low-level vision.

Knowledge graph completion (KGC) is dedicated to deducing absent facts within incomplete knowledge graphs (KGs). The majority of antecedent studies exclusively address the transductive scenario, wherein all entities are observed throughout the training process, which is impractical for the everyday reality with constantly emerging entities. Recent works that concern this issue mainly focus on representing emerging entities with seen neighbors while ignoring the semantics in the queried relations. In this paper, we introduce a model named DKGC, which incorporates two purposefully designed entity encoding modules to capture the local and global entity representations in a relation-specific manner and a sampling module with a diffusion process to explore the potential semantics further. Extensive experiments on benchmark datasets indicate the superiority of our model for inductive KGC.

Vehicle driving behavior analysis and detection tasks have become an indispensable part of intelligent transportation systems. Accurate pattern recognition of potential anomalies during the movement of entities is crucial for improving transportation efficiency. Current methods typically analyze vehicle trajectories independently without considering potential interactions among vehicles. To address this limitation, some studies have integrated graph attention mechanisms to capture the influence of neighboring vehicles during the aggregation process. However, Graph Attention Networks (GATs) are constrained by the univariate nature of attention heads and coefficients, thus lacking flexibility. In this work, we not only consider the social dynamics among neighboring vehicles but also delve into the limitations of GAT models. We propose a Vehicular Social Dynamics Anomaly Detection (VSD-AD) model based on the Recurrent Multi-Mask Aggregator (MMA) enabled Variational AutoEncoder (VAE) architecture to maximize the learning of relational embeddings among neighbors in a highway vehicle network. Furthermore, we apply Node Feature Quantisation (NFQ) to the encoder output to mitigate the complexity of neighbor relationships. Our model is flexible and customizable for different highway scenarios, suitable for large-scale highway vehicle video data. To validate real-world applicability, we further assess its performance on both the simulated dataset and real-world traffic dataset, where our model outperforms other mainstream methods in terms of detection performance.

Formula retrieval is an important research topic in Mathematical Information Retrieval (MIR).Most studies have focused on comparing formulae to determine the similarity between mathematical documents.However, two similar formulae may appear in completely different knowledge domains and have different meanings.Based on N-ary Tree-based Formula Embedding Model (NTFEM), we introduce a new hybrid retrieval model combining formula with its surrounding text for more accurate retrieval.Using keywords extraction technology, we extract keywords from text around the formula which can supplement the semantic information of formula.Then we get the representation vectors of keywords by FastText N-gram embedding model, and the representation vectors of formulae by NTFEM.Finally, documents are first sorted according to the similarity of keywords, and then the ranking results are optimized by formula similarity.Experimental results show that the accuracy of top-10 results is at least 20% higher than that of NTFEM and can be 50% in some specific topics.

Formula retrieval is an important research topic in Mathematical Information Retrieval (MIR). Most studies have focused on formula comparison to determine the similarity between mathematical documents. However, two similar formulae may appear in entirely different knowledge domains and have different meanings. Based on N-ary Tree-based Formula Embedding Model (NTFEM, our previous work in [Y. Dai, L. Chen, and Z. Zhang, An N-ary tree-based model for similarity evaluation on mathematical formulae, in Proc. 2020 IEEE Int. Conf. Systems, Man, and Cybernetics, 2020, pp. 2578–2584.], we introduce a new hybrid retrieval model, NTFEM-K, which combines formulae with their surrounding keywords for more accurate retrieval. By using keywords extraction technology, we extract keywords from context, which can supplement the semantic information of the formula. Then, we get the vector representations of keywords by FastText N-gram embedding model and the vector representations of formulae by NTFEM. Finally, documents are sorted according to the similarity between keywords, and then the ranking results are optimized by formula similarity. For performance evaluation, NTFEM-K is not only compared with NTFEM but also hybrid retrieval models combining formulae with long text and hybrid retrieval models combining formulae with their keywords using other keyword extraction algorithms. Experimental results show that the accuracy of top-10 results of NTFEM-K is at least 20% higher than that of NTFEM and can be 50% in some specific topics.

Shifting from single-person experiences to multi-user interactions is an inevitable trend of virtual reality technology. Existing methods primarily address the problem of one- or two-user redirected walking and do not respond to additional challenges related to potential collisions among three or more users who are moving both virtually and physically. To apply redirected walking to multiple users who are immersed in virtual reality experiences, we present a novel algorithm of three-user redirected walking in shared physical spaces. In addition, we present the steps to apply three-user redirected walking to multiplayer VR scene, where the users are divided into different groups based on the users' motion states. Therefore, this strategy can be applied to each group to address the challenges of redirected walking when there are more than three users. The results show that sharing a space using our three-user redirected walking algorithm is completely feasible.