The Experts below are selected from a list of 49545 Experts worldwide ranked by ideXlab platform
Lappui Chau - One of the best experts on this subject based on the ideXlab platform.
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a rain pixel Recovery Algorithm for videos with highly dynamic scenes
IEEE Transactions on Image Processing, 2014Co-Authors: Jie Chen, Lappui ChauAbstract:Rain removal is a very useful and important technique in applications such as security surveillance and movie editing. Several rain removal Algorithms have been proposed these years, where photometric, chromatic, and probabilistic properties of the rain have been exploited to detect and remove the rainy effect. Current methods generally work well with light rain and relatively static scenes, when dealing with heavier rainfall in dynamic scenes, these methods give very poor visual results. The proposed Algorithm is based on motion segmentation of dynamic scene. After applying photometric and chromatic constraints for rain detection, rain removal filters are applied on pixels such that their dynamic property as well as motion occlusion clue are considered; both spatial and temporal informations are then adaptively exploited during rain pixel Recovery. Results show that the proposed Algorithm has a much better performance for rainy scenes with large motion than existing Algorithms.
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efficient motion vector Recovery Algorithm for h 264 based on a polynomial model
IEEE Transactions on Multimedia, 2005Co-Authors: Jinghong Zheng, Lappui ChauAbstract:In this paper, we propose an efficient motion vector Recovery Algorithm for the new coding standard H.264, which is based on a polynomial model. To achieve better coding efficiency, the motion estimation scheme used in H.264 is different from previous coding standards. In H.264, a 16/spl times/16 macroblock can be divided into different block shapes for motion estimation. Each macroblock contains more motion vectors than previous coding standards. For nature video, the blocks within a small area likely belong to the same object, hence the motion vectors of neighboring blocks are highly correlated. Based on the correlation of neighboring motion vectors, we can use the motion vectors that are adjacent to the lost motion vectors to constitute a polynomial model, which can describe the change tendency of motion vectors within a small area. Through this model, the lost motion vectors can be predicted and the lost macroblocks can be reconstructed. Different video sequences are used to test the performance of proposed method. The simulation results show that the quality of corrupted video can be obviously improved by proposed Algorithm.
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a motion vector Recovery Algorithm for digital video using lagrange interpolation
IEEE Transactions on Broadcasting, 2003Co-Authors: Jinghong Zheng, Lappui ChauAbstract:In this paper, we propose an efficient motion vector Recovery Algorithm for the new coding standard H.264, which makes use of the Lagrange interpolation formula. In H.264, a 16/spl times/16 inter macroblock can be divided into different block shapes for motion estimation, and each block has its own motion vector. For nature video the movement within a small area is likely to move in the same direction, hence the neighboring motion vectors are correlative. Because the motion vector in H.264 covers smaller area than previous coding standards, the correlation between neighboring motion vectors increases. We can use the Lagrange interpolation formula to constitute a polynomial that describes the motion tendency of motion vectors, which are next to the lost motion vector, and use this polynomial to recover the lost motion vector. The simulation result shows that our Algorithm can efficiently improve the visual quality of corrupted video.
Jiwu Huang - One of the best experts on this subject based on the ideXlab platform.
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authentication and Recovery Algorithm for speech signal based on digital watermarking
Signal Processing, 2016Co-Authors: Zhenghui Liu, Fan Zhang, Jing Wang, Hongxia Wang, Jiwu HuangAbstract:A content authentication and tamper Recovery scheme for digital speech signal is proposed. In this paper, a new compression method for speech signal based on discrete cosine transform is discussed, and the compressed signals obtained are used to tamper Recovery. One block-based large capacity embedding method is explored, which is used for embedding the compressed signals. For the scheme proposed, watermark is generated by frame number and compressed signal. If watermarked speech is attacked, the attacked frames can be located by frame number, and reconstructed by using the compressed signal. Theoretical analysis and experimental results demonstrate that the scheme not only improves the security of watermark system, but also can locate the attacked frames precisely and reconstruct the attacked frames. Speech compression.Block-based high-capacity embedding method.Tamper Recovery.
Wu Quanyuan - One of the best experts on this subject based on the ideXlab platform.
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A Log-based Recovery Algorithm Based on Fault-tolerant Web Services
Computer Engineering, 2005Co-Authors: Wu QuanyuanAbstract:Traditional fault-tolerant systems which use log-based Recovery Algorithm often lost the request.This article describes a log-based Algorithm based on passive replication.The main idea of the Algorithm is classifying the message when logging and recovering.At last the correctness of this Algorithm is proved by experiment.
Wang Xuan - One of the best experts on this subject based on the ideXlab platform.
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Wireless sensor networks data Recovery Algorithm based on quadratic programming
Journal of Computer Applications, 2013Co-Authors: Wang XuanAbstract:For improving the real-time performance of Recovery Algorithm in Compressed Sensing(CS) of Wireless Sensor Networks(WSN) data,a quadratic programming based network data Recovery Algorithm was proposed in this paper.The CS Recovery was transformed to bound-constrained quadratic programming,and then the network data was recovered by solving the quadratic programming problem based on the Armijo rule.The analysis and experimental results demonstrate that the proposed Algorithm can significantly reduce the complexity and ensure the accuracy of Recovery,thus improving the real-time performance of data Recovery in WSN.
Feng Tong - One of the best experts on this subject based on the ideXlab platform.
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Mean-square analysis of the gradient projection sparse Recovery Algorithm based on non-uniform norm
Neurocomputing, 2017Co-Authors: Feng TongAbstract:Abstract With the previously proposed non-uniform norm called l N -norm, which consists of a sequence of l 1 -norm or l 0 -norm elements according to relative magnitude, a novel l N -norm sparse Recovery Algorithm can be derived by projecting the gradient descent solution to the reconstruction feasible set. In order to gain analytical insights into the performance of this Algorithm, in this letter we analyze the steady state mean square performance of the gradient projection l N -norm sparse Recovery Algorithm in terms of different sparsity, as well as additive noise. Numerical simulations are provided to verify the theoretical results.
