Shuo-Fei Wang

Wen-Kai Yu, Ya-Xin Li, Jian Leng et al.

A secure optical communication requires both high transmission efficiency and high authentication performance, while existing cryptographic key distribution protocols based on ghost imaging have many shortcomings. Here, based on computational ghost imaging, we propose an interactive protocol that enables multi-party cryptographic key distribution over a public network and self-authentication by setting an intermediary that shares partial roles of the server. This fragment-synthesis-based authentication method may facilitate the remote distribution of cryptographic keys.

Shuo-Fei Wang, Wen-Kai Yu, Ya-Xin Li

Networks with large receptive field (RF) have shown advanced fitting ability in recent years. In this work, we utilize the short-term residual learning method to improve the performance and robustness of networks for image denoising tasks. Here, we choose a multi-wavelet convolutional neural network (MWCNN), one of the state-of-art networks with large RF, as the backbone, and insert residual dense blocks (RDBs) in its each layer. We call this scheme multi-wavelet residual dense convolutional neural network (MWRDCNN). Compared with other RDB-based networks, it can extract more features of the object from adjacent layers, preserve the large RF, and boost the computing efficiency. Meanwhile, this approach also provides a possibility of absorbing advantages of multiple architectures in a single network without conflicts. The performance of the proposed method has been demonstrated in extensive experiments with a comparison with existing techniques.