Sorry, you need to enable JavaScript to visit this website.

An efficient video dehazing system based on the dark channel prior (DCP) is proposed. For the proposed system, the original DCP-based dehazing method is modified in order to achieve a fast execution of the airlight estimation without noticeable degradation of the dehazing quality. The proposed system is implemented in a heterogeneous multi-processor system-on-chip, for which the overall dehazing process is described in OpenCL and synthesized into the custom circuitry targeting an FPGA.

We present an efficient multi-image super resolution (MISR) method. Our solution consists of a L1-norm optimized reconstruction scheme for super resolution (SR), and a three-layer convolutional network for artifacts removal, in a concatenated fashion. Such a two-stage method achieves excellent performance, which outperforms the existing state-of-the-art SR methods in both subjective and objective measurements (e.g., 5 to 7 dB improvements on popular image database using PSNR metric).

Data ecosystems are becoming larger and more complex, while privacy concerns are threatening to erode their potential benefits. Recently, users have developed obfuscation techniques that issue fake search engine queries, undermine location tracking algorithms, or evade government surveillance. These techniques raise one conflict between each user and the machine learning algorithms which track the users, and one conflict between the users themselves. We use game theory to capture the first conflict with a Stackelberg game and the second conflict with a mean field game.

In this paper, we consider the design of optimal transceiver and relay processing algorithms for a full-duplex (FD) two-way amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying system. We assume the channel state information of loopback self-interference (SI) channels to be imperfect. The nodes employ precoders and receive filters for suppressing the residual SI. The optimal precoders at transceivers are designed by equalising the signal-to-interference-plus-noise ratio at the relay and transceiver.

In this paper, we propose a differentially-private canonical correlation analysis algorithm. Canonical correlation analysis (CCA) is often used in clustering applications for multi-view data. CCA finds subspaces for each view such that projecting each of the views onto these subspaces simultaneously reduces the dimension and maximizes correlation. Differential-privacy is a framework for understanding the risk of inferring the data input to the algorithm based on the output.