Unified image restoration has become a fundamental issue in image processing. State space models have demonstrated significant potential in image restoration. However, their multi-directional scanning mechanism may introduce computational and feature redundancy, failing to satisfy lightweight deployment requirements. Furthermore, state space models have limitations in perceiving local detail features. To address this, we propose a lightweight channel-adaptive temporal-frequency sparse state space model for unified image restoration. This model enhances the local detail perception capability of the state space model using frequency domain features and simplifies the complexity of the network by sparse mechanisms. Specifically, we designed a U-shaped image restoration deep network based on the channel-adaptive temporal-frequency sparse state space module. This module consists of a temporal-domain dynamic sparse visual state space module and a frequency-domain sparse wavelet detail enhancement module in parallel, and uses a channel shuffling operation to realize temporal-frequency feature fusion. The dynamic sparse state space module uses a top-k mechanism to sparsify features across different scan paths for computational efficiency. The frequency-domain sparse wavelet detail enhancement module utilizes wavelet transformation and convolution operations to extract and enhance details in different directions, and then uses a top-k mechanism to perform sparse processing. Moreover, we introduce a degradation semantic perception module at the end of the encoder to guide the restoration network to adaptively learn the semantics of different degradation types, thereby realizing unified image restoration in complex outdoor environments. Extensive experimental results demonstrate that our method significantly outperforms 31 baseline methods in five complex weather and illumination degradation image restoration tasks while maintaining the lowest parameters and FLOPs.

In the era of big data, the demand for spatial dataset search has become increasingly urgent. Leveraging the powerful storage and computing capabilities of cloud platforms, the cloud has become a common choice for deploying dataset search services. However, under risks of untrusted cloud environment and malicious attacks, protecting the privacy of sensitive location information during spatial dataset search becomes particularly critical. This paper focuses on the problem of privacy-preserving spatial datasets joinable search in cloud, which has not been addressed in existing research. We first propose a grid-based joinable coverage distinction model to measure the joinability of spatial datasets, and further present a baseline scheme (PDJDS). To further enhance efficiency and reduce storage cost, we propose an optimized scheme (PDJDS+), which constructs a coarse-grained grid-based inverted index to filter candidate datasets and integrates a joinable coverage distinction check table to expedite the evaluation of spatial dataset coverage distinction. Experiments conducted on three real-world spatial data repositories demonstrate that our scheme achieves superior performance in terms of search accuracy, efficiency, and storage cost.

ABSTRACT Multi‐input reconstruction methods such as 3D‐GS and NeRF excel in fidelity, yet they impose stringent requirements on the sequentiality of the input images. In contrast, single‐view reconstruction methods are designed to extract certain features of the image even under limited input conditions. However, the majority of current single‐view methods demand considerable graphics card performance for rendering at high resolutions and attaining high‐fidelity image reconstruction at lower resolutions remains a formidable challenge. To enhance the fidelity of reconstructed images while considering the constraints of graphics card performances, we propose a novel pipeline based on novel‐view synthetic (NVS), super‐resolution (SR) and 3D‐GS, named RSINS‐GS. First, we introduce a divide‐and‐conquer strategy tailored to reap pixel‐reinforced novel sequential views to render the reconstruction result without overburdening the graphics card, maintaining optimal performance and visual fidelity. Furthermore, to enhance the high fidelity of reconstructed images both in terms of qualitative and quantitative measures, we integrate 2D prior images with their corresponding geometric structural complements. Additionally, we introduce an innovative, generalised noise‐added strategy to refine the overall reconstruction process. Extensive experimental evaluations on Nerf_synthetic datasets and Google scanned datasets show that our method achieves high quality results.

In the field of remote sensing change detection (RSCD), accurately identifying significant changes between bi-temporal images is essential for environmental monitoring, urban planning, and disaster assessment. In recent years, advancements in deep learning for computer vision (CV) have transformed RSCD, significantly enhancing its effectiveness. However, existing methods often overlook the importance of depth information, focusing primarily on 2-D information. This limits their ability to capture subtle changes and structural details in 3-D space. To address these limitations, we introduce ChangeDA—a depth-augmented multitask network designed to enhance the effectiveness of RSCD. ChangeDA introduces a depth encoder module to extract implicit depth information from optical images, enabling the utilization of 3-D structural information without reliance on external data sources. Through the depth infusion module (DIM), depth information is integrated into the dual-temporal feature maps, significantly enhancing the network’s ability to perceive changes in 3-D spatial structures. In addition, ChangeDA includes a differential feature extractor (DFE) tailored to pinpoint differential features between sequential images, and an adaptive all-feature fusion (AAFF) strategy that significantly improves recognition accuracy and generalization capability through cross-level feature integration. Performance evaluations on four prominent single-modal datasets—LEVIR-CD, S2Looking, WHU-CD, and SYSU-CD—yielded state-of-the-art (SOTA)${F}1$-scores of 92.27%, 66.42%, 94.12%, and 82.74%, respectively. Furthermore, ChangeDA also achieved outstanding results on the multimodal 3DCD dataset, with an${F}1$score of 63.52% in 2-D CD and an RMSE of 1.20 in the 3-D CD task. These results demonstrate ChangeDA’s robust adaptability across diverse targets and real-world scenarios.

With the expansion of mobile devices and cloud computing, massive spatial trajectory data is generated and outsourced to the cloud for storage and analysis, enabling location-based mobile computing services. However, due to the sensitivity of the trajectory data, sharing it in plaintext could lead to privacy risks, especially in operations like contact queries. Thus, achieving secure and efficient contact queries based on the trajectory data in the cloud is a significant challenge. In this paper, we propose a privacy-preserving contact query processing over trajectory data in mobile cloud computing. The projection-based secure trajectory encoding is designed to convert trajectories into secure codes such that the comparison between the distance of two moving objects and the contact distance threshold is transformed into a problem of secure code matching. Adopting the secure code matching method, a baseline privacy-preserving contact query processing is proposed. To improve the query accuracy and efficiency, an amplification factor, an HTG-index and a filter table are designed for query processing optimization, based on which an enhanced privacy-preserving contact query processing is proposed. The game stimulation-based security analysis and experimental results show that the proposed query scheme is secure and performs well in query accuracy and efficiency.

The development of cloud computing has met the growing demand for dataset search in the era of massive data. In the field of spatial dataset search, the high prevalence of sensitive information in spatial datasets underscores the necessity of privacy-preserving search processing in the cloud. However, existing spatial dataset search schemes are designed on plaintext datasets and do not consider privacy protection in search processing. In this paper, we first propose a privacy-preserving spatial dataset search scheme. The density distribution-based similarity model is proposed to measure the similarity between spatial datasets, and then the order-preserving encrypted similarity is designed to achieve secure similarity calculation. With the above idea, the baseline search scheme (PriDAS) is proposed. To improve the search efficiency, a two-layer index is designed to filter candidate datasets and accelerate the similarity calculation between datasets. By using the index, the optimized search scheme (PriDAS+) is proposed. To analyze the security of the proposed schemes, the game simulation-based proof is presented. Experimental results on three real-world spatial data repositories with 100,000 spatial datasets show that PriDAS+ only needs less than 0.4 seconds to accomplish the search processing.

During infectious disease outbreaks, tracking contacted objects is important for suppressing the spread of the virus and using trajectories of moving objects to discover contacted objects is one of effective approaches. Existing algorithms focus on individual contact event discovery but lack the ability to obtain the k-degree contact events. In this paper, we propose efficient k-degree contact pattern mining algorithms that are capable of mining k-degree contact events. The definition of k-degree contact event is first formulated. Based on the definition, a sliding window-based baseline k-degree contact pattern mining algorithm (KCPMA) is presented. To improve the mining efficiency, the sample-point checking strategy and R-tree index are adopted and the optimized mining algorithm (KCPMA+) is proposed. Comprehensive experiments on real datasets demonstrate that the proposed algorithms are effective and efficient in mining k-degree contact events.

With the significant increase in open spatial datasets, there is a growing need to search for datasets that meet users’ requirements for decision-making and machine learning. This has become a prominent issue, leading to various spatial dataset search requirements, including the need for spatial dataset range search. In this paper, we propose spatial dataset range search schemes, which is the first systematic study of spatial dataset range search processing according to the best of our knowledge. A baseline spatial dataset range search scheme is first proposed to process spatial dataset range searches. To improve the search efficiency, we proposed two optimized search schemes, the accuracy-first optimized search scheme and the efficiency-first optimized search scheme. In the former optimized scheme, the spatial dataset-MBR-based R-tree (SDMR-tree) is designed to filter candidate datasets without compromising search accuracy. In the latter optimized scheme, the dataset-grid inverted index (DGI-index) storing the spatial dataset grid distributions is designed and used to determine the search result approximately. The search efficiency is further improved but with a bit loss of accuracy. Comprehensive experiments on real-world data validate the accuracy and efficiency of the proposed search schemes.

The era of data explosion has brought a significant increase in demand for dataset search. However, deployment of spatial dataset search services in the cloud suffers from privacy leakage and search effectiveness issues. To resolve this problem, we first propose an exemplar privacy-preserving spatial dataset search scheme (EDSS) in this paper. EDSS extracts and encrypts the grid distribution of spatial datasets as metadata to enable the search processing on encrypted spatial datasets. Experimental results using three real-world spatial data repositories demonstrate that the proposed scheme can effectively implement exemplar spatial dataset search.

Image highlight removal is a fundamental and challenging visual task. Although fully supervised deep learning-based methods have achieved remarkable results, their performance is limited by the diversity and quantity of paired highlight images. To address this issue, we propose a student-teacher semi-supervised deep learning method based on contrastive learning for highlight removal, which integrates paired and unpaired data for boosting image highlight removal. Specifically, our semi-supervised network consists of the parallel student and teacher sub-networks with the same U-shaped CTransformer. The CTransformer integrates a dual multiscale convolution module and a parallel multiaxial self-attention module to promote local feature representation and global contextual semantic comprehension of the network. The dual multiscale convolution module realizes the representation of multiple perceptual fields by the internal and external multiscales. The parallel multiaxial self-attention module implements multidimensional autocorrelation attention with the selective fusion mechanism. Quantitative and qualitative results show that our method takes SOAT results on different datasets.

Link prediction in complex networks has been one of the important issue within the realm of data mining and science analysis. Therefore, it wins an increasing attention and many link prediction based on similarity-based algorithm have proposed so far. The traditional similarity-based algorithms only make use of common neighbors having many shared feature to predict the future structure in complex network, but it ignored the influence of predicting nodes. The greater the influence of node the higher the likelihood that the node will be selected as future links between two ends. In this paper, an efficient method is proposed by combining influence of predicting nodes and clustering coefficient of common neighbors to improve the accuracy and applicability of link prediction for complex networks. The influence of nodes can be expressed by node centrality, which is a local centrality to identify influential nodes by combining the degree of the target node and clustering coefficients of second-order neighbors. A comparison between the proposed method and other similarity-based algorithms has been performed, and results have been reported for nine real-world datasets. The results further validate that had a higher prediction accuracy compared with other link prediction approaches.

Using trajectories of moving objects and performing contact event query during disease transmission is an effective method of prevention and control. Existing contact query processing algorithms only consider single-source (one-to-one) contact event and thus can not discover multi-source (n-to-one) contact events. In this paper, we propose efficient multi-source contact event query processing methods that are capable of querying multi-source contact events. The definition of multi-source contact events is first formulated. Then, a baseline multi-source contact event query processing algorithm is presented, which adopts the idea of sliding window-based sequential scanning. To improve the query efficiency, the 2-dimensional bitmap filter and the anchor time point scanning are designed and used in the optimized query processing algorithm. Comprehensive experiments on real-world data demonstrate that the proposed algorithms can find more potential contact events and have good performance in the manner of query time cost.

In this paper, we propose a retinex-based decomposition model for a hazy image and a novel end-to-end image dehazing network. In the model, the illumination of the hazy image is decomposed into natural illumination for the haze-free image and residual illumination caused by haze. Based on this model, we design a deep retinex dehazing network (RDN) to jointly estimate the residual illumination map and the haze-free image. Our RDN consists of a multiscale residual dense network for estimating the residual illumination map and a U-Net with channel and spatial attention mechanisms for image dehazing. The multiscale residual dense network can simultaneously capture global contextual information from small-scale receptive fields and local detailed information from large-scale receptive fields to precisely estimate the residual illumination map caused by haze. In the dehazing U-Net, we apply the channel and spatial attention mechanisms in the skip connection of the U-Net to achieve a trade-off between overdehazing and underdehazing by automatically adjusting the channel-wise and pixel-wise attention weights. Compared with scattering model-based networks, fully data-driven networks, and prior-based dehazing methods, our RDN can avoid the errors associated with the simplified scattering model and provide better generalization ability with no dependence on prior information. Extensive experiments show the superiority of the RDN to various state-of-the-art methods.