Han Bin

TL;DR: In this article, a speech noise reduction method using a traditional signal processing method and a neural network model are combined to complement each other, first, a traditional SPM method is used to process an audio signal, a part of uniform environmental noise such as white noise can be filtered, a subsequent NN model can mainly deal with other noises such as carwhistle and other person sounds, at the same time, a VAD operation is performed on a noisy speech sample, unnecessary signals are removed, and noise reduction can be trained to a certain extent.

TL;DR: Numerical experiments confirm that the proposed multiple sub-wavelet-dictionaries-based adaptive adaptive iteratively weighted regularization algorithm can significantly improve the recovery performance than the previous works.

TL;DR: Wang et al. as mentioned in this paper proposed a video image recompression method by combining the existing video image compression method and depth learning technology, and the compression proportion of the intra-frame data of the video image can be greatly improved.

TL;DR: An effective combination policy is proposed, which is named as relative-deviation combination policy and uses the Euclidean norm of instantaneous deviation between the intermediate estimation vector of alone agent and the fused estimation weight to determine the combination weights of each neighbor.