Zhiqiang Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Zhiqiang Zhang has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Fang Han
School of Mathematics and Statistics, Ningxia University, Yinchuan, China
1 shared publication - Rong Huang
College of Information Science and Technology, Donghua University
1 shared publication - Zhijie Wang
College of Information Science and Technology, Donghua University, Shanghai, China
1 shared publication
Latest Publications
- Open Access
- 19 April 2019
- Published by MDPI AG in Algorithms
- Vol. 12 (4) , 82
Image Error Concealment Based on Deep Neural Network
In this paper, we propose a novel spatial image error concealment (EC) method based on deep neural network. Considering that the natural images have local correlation and non-local self-similarity, we use the local information to predict the missing pixels and the non-local information to correct the predictions. The deep neural network we utilize can be divided into two parts: the prediction part and the auto-encoder (AE) part. The first part utilizes the local correlation among pixels to predict the missing ones. The second part extracts image features, which are used to collect similar samples from the whole image. In addition, a novel adaptive scan order based on the joint credibility of the support area and reconstruction is also proposed to alleviate the error propagation problem. The experimental results show that the proposed method can reconstruct corrupted images effectively and outperform the compared state-of-the-art methods in terms of objective and perceptual metrics.
- 19 April 2019
- Published by MDPI AG in Algorithms
- Vol. 12 (4) , 82
Open AccessImage Error Concealment Based on Deep Neural Network
In this paper, we propose a novel spatial image error concealment (EC) method based on deep neural network. Considering that the natural images have local correlation and non-local self-similarity, we use the local information to predict the missing pixels and the non-local information to correct the predictions. The deep neural network we utilize can be divided into two parts: the prediction part and the auto-encoder (AE) part. The first part utilizes the local correlation among pixels to predict the missing ones. The second part extracts image features, which are used to collect similar samples from the whole image. In addition, a novel adaptive scan order based on the joint credibility of the support area and reconstruction is also proposed to alleviate the error propagation problem. The experimental results show that the proposed method can reconstruct corrupted images effectively and outperform the compared state-of-the-art methods in terms of objective and perceptual metrics.