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Traffic volume data is already collected and used for a variety of purposes in intelligent transportation system (ITS). However, the collected data might be abnormal due to the problem of outlier data caused by malfunctions in data collection and record systems. To fully analyze and operate the collected data, it is necessary to develop a validate method for addressing the outlier data. Many existing algorithms have studied the problem of outlier recovery based on the time series methods. In this paper, a multiway tensor model is proposed for constructing the traffic volume data based on the intrinsic multilinear correlations, such as day to day and hour to hour. Then, a novel tensor recovery method, called ADMM-TR, is proposed for recovering outlier data of traffic volume data. The proposed method is evaluated on synthetic data and real world traffic volume data. Experimental results demonstrate the practicability, effectiveness, and advantage of the proposed method, especially for the real world traffic volume data.

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Nowadays, more and more researchers are interested in reversible data hiding in encrypted images (RDHEI), which can be applied in privacy protection and cloud storage. In this paper, a new RDHEI method on the basis of hierarchical quad-tree coding and multi-MSB (most significant bit) prediction is proposed. The content owner performs pixel prediction to obtain a prediction error image and explores the maximum embedding capacity of the prediction error image by hierarchical quad-tree coding before image encryption. According to the marked bits of vacated room capacity, the data hider can embed additional data into the room-vacated image without knowing the content of original image. Through the data hiding key and the encryption key, the legal receiver is able to conduct data extraction and image recovery separately. Experimental results show that the average embedding rates of the proposed method can separately reach 3.504 bpp (bits per pixel), 3.394 bpp, and 2.746 bpp on three well-known databases, BOSSBase, BOWS-2, and UCID, which are higher than some state-of-the-art methods.

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The problem of recovering data in multi-way arrays (i.e., tensors) arises in many fields such as image processing and computer vision, etc. In this paper, we present a novel method based on multi-linear n-rank and norm optimization for recovering a low-n-rank tensor with an unknown fraction of its elements being arbitrarily corrupted. In the new method, the n-rank and norm of the each mode of the given tensor are combined by weighted parameters as the objective function. In order to avoid relaxing the observed tensor into penalty terms, which may cause less accuracy problem, the minimization problem along each mode is accomplished by applying the augmented Lagrange multiplier method. The proposed approach is evaluated both on simulated data and real world data. Experimental results show that our proposed method tends to deliver higher-quality solutions with faster convergence rate compared with previous methods.