本文已被：浏览 830次 下载 423次
投稿时间：2020-02-28
投稿时间：2020-02-28
中文摘要: 图像哈希是通过将图像提取为简短数列，从而快速地从图库中区分出与原图相似或不同图像的方法。利用奇异值分解（SVD）来分解重构减小图像信息的冗余性和二维变分模态分解（2D-VMD）可以将图像分解成一系列不同中心频率的子模态的特性，从时域和频域提取出图像的主要信息序列来构成哈希。仿真结果表明，相比于其他方法，通过SVD和2D-VMD的紧凑图像哈希算法具有较短的运行时间、较好的鲁棒性和唯一性。
Abstract:Image hashing is a method to quickly distinguish images similar to or different from the original image from a gallery by extracting images into short sequences.Singular value decomposition (SVD) is used to reduce the redundancy of image information and 2D variational mode decomposition (2D-VMD) can decompose the image into a series of sub-modes of different center frequencies, and extract the main information sequence of image from time domain and frequency domain to form hash.The simulation results show that this method has shorter running time, better robustness and uniqueness than other methods.
文章编号：20213017 中图分类号：TP391 文献标志码：
基金项目:国家自然科学基金（61802250）。
引用文本：
赵琰,周晓炜.结合奇异值分解和二维变分模态分解的紧凑图像哈希算法[J].上海电力大学学报,2021,37(3):295-302.
ZHAO Yan,ZHOU Xiaowei.A Compact Image Hash Algorithm Combining SVD and Two-dimensional Variational Mode Decomposition[J].Journal of Shanghai University of Electric Power,2021,37(3):295-302.
赵琰,周晓炜.结合奇异值分解和二维变分模态分解的紧凑图像哈希算法[J].上海电力大学学报,2021,37(3):295-302.
ZHAO Yan,ZHOU Xiaowei.A Compact Image Hash Algorithm Combining SVD and Two-dimensional Variational Mode Decomposition[J].Journal of Shanghai University of Electric Power,2021,37(3):295-302.