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Section: Publications & Resources
Publication: IEEE Transactions on Computational Imaging
Three-dimensional (3-D) radar imaging can provide additional information along elevation dimension about the target with respect to the conventional 2-D radar imaging, but usually requires a huge amount of data collected over 3-D frequency-azimuth-elevation space, which motivates us to perform 3-D imaging by using sparsely sampled data. Traditional compressive sensing (CS) based 3-D imaging methods with sparse data convert the 3-D data into a long vector, and then complete the sensing and recovery steps.
Section: Publications & Resources
Publication: IEEE Transactions on Computational Imaging
Good temporal representations are crucial for video understanding, and the state-of-the-art video recognition framework is based on two-stream networks. In such framework, besides the regular ConvNets responsible for RGB frame inputs, a second network is introduced to handle the temporal representation, usually the optical flow (OF).
Section: Publications & Resources
Publication: IEEE Transactions on Computational Imaging
Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements. Dictionary or transform learning methods are useful in applications such as denoising, inpainting, and medical image reconstruction.
Section: Publications & Resources
In this paper, an agile smart attacker model in spectrum sensing of cognitive radio network (CRN) is introduced. This smart attacker does not make the channel busy all the time, instead it senses spectrum and when a primary user leaves, it occupies the spectrum by mimicking the signal characteristics of the primary users.
Section: Publications & Resources
We consider a decentralized detection network whose aim is to infer a public hypothesis of interest. However, the raw sensor observations also allow the fusion center to infer private hypotheses that we wish to protect. We consider the case where there are an uncountable number of private hypotheses belonging to an uncertainty set, and develop local privacy mappings at every sensor so that the sanitized sensor information minimizes the Bayes error of detecting the public hypothesis at the fusion center while achieving information privacy for all private hypotheses.
Section: Publications & Resources
In this paper, we investigate beamforming design for cooperative secure transmission in cognitive two-way relay networks, where the cognitive transmitter (CT) with multiple antennas helps to forward the signals of two primary transmitters (PTs) and tries to protect the PTs from wiretapping by a single-antenna eavesdropper.
Section: Publications & Resources
Publication: IEEE Transactions on Image Processing
The inverse synthetic aperture radar (ISAR) imaging technique of a moving target with sparse sampling data has attracted wide attention due to its ability to reduce the data collection burden. However, traditional low-rank or 2D compressive sensing (CS)-based ISAR imaging methods can handle the random sampling or the separable sampling data only.
Section: Publications & Resources
Publication: IEEE Transactions on Image Processing
Image semantic segmentation tasks and methods based on weakly supervised conditions have been proposed and achieve better and better performance in recent years. However, the purpose of these tasks is mainly to simplify the labeling work. In this paper, we establish a new and more challenging task condition.
Section: Publications & Resources
Publication: IEEE Transactions on Image Processing
We present a method to compress geometry information of point clouds that explores redundancies across consecutive frames of a sequence. It uses octrees and works by progressively increasing the resolution of the octree. At each branch of the tree, we generate an approximation of the child nodes by a number of methods which are used as contexts to drive an arithmetic coder.
Section: Publications & Resources
Publication: IEEE Transactions on Multimedia
The scalable high efficiency video coding (SHVC) is an extension of high efficiency video coding (HEVC). It introduces multiple layers and inter-layer prediction, thus significantly increases the coding complexity on top of the already complicated HEVC encoder. In inter prediction for quality SHVC, in order to determine the best possible mode at each depth level, a coding tree unit can be recursively split into four depth levels.
