Noise Removal Using Cohen-Grossberg Neural Network for Improving the Quality of the Decrypted Image in Color Encryption

摘要：

In this paper, a color image encryption method is proposed with the removal of noise generated during the transmission based on Cohen-Grossberg neural networks, where the color image is expressed in terms of the standard red-green-blue (RGB) space, and the corresponding pixel matrix is hidden by Arnold transform (AT). The Cohen-Grossberg neural network is added to store the hidden message as the stable equilibria, which achieves the noise removal. The hidden message without noise is recovered by performing AT with accurate iteration numbers. Experimental results show that the proposed method achieves effective resistance against transmission noise.

关键词： Color Image encryption Noise removal Arnold transform Cohen-Grossberg neural network

作者: Yanling Liu Jianxiong Zhang Wansheng Tang

作者单位: Institute of Systems Engineering Tianjin University Tianiin. China Institute of Systems Engineering Tianjin University Tianjin, China

会议类型: 国际会议

会议名称: 2011 2nd International Conference on Data Storage and Data Engineering(DSDE 2011)(2011年第二届数据存储与数据工程国际会议)

会议地点: 西安

会议语种:英文

页码: 25-29

在线出版日期: 2011-05-13（万方平台首次上网日期，不代表论文的发表时间）