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基于小波变换的数字图像双重随机置乱加密 被引量:4
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作者 孙秋冬 顾永杰 +1 位作者 马文新 颜文英 《上海第二工业大学学报》 2008年第4期270-275,共6页
提出了一个用于图像加密的基于小波变换的双重随机置乱算法。算法首先对图像进行小波分解;再对分解结果进行第一次随机置乱。这一置乱有两种方案:第一种是直接对每一分量图像分别进行置乱,第二种是对由所有分量图像拼接而成的图像进行置... 提出了一个用于图像加密的基于小波变换的双重随机置乱算法。算法首先对图像进行小波分解;再对分解结果进行第一次随机置乱。这一置乱有两种方案:第一种是直接对每一分量图像分别进行置乱,第二种是对由所有分量图像拼接而成的图像进行置乱,置乱后再分割成与原分量位置与大小一致的分量图像;然后对这些分量图像进行小波重构,并对重构结果再次进行随机置乱,得双重随机置乱加密图像。实验中,以统计直方图和视觉效果对两种方法作了比较。比较结果表明两种方法的视觉置乱效果都不错,但由第一种方法置乱所得图像的直方图与原图很相似,说明该方法是以位置置乱为主,而由第二种方法置乱所得图像的直方图则与原图差别很大,说明该方法不仅有位置置乱,而且灰度级置乱更明显。可见,第二种置乱方法更有效。 展开更多
关键词 数字图像 图像加密:小波变换:双重随机置乱 两种方案
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AN APPLIED RESEARCH ON APPROACH OF DYADIC WAVELET TRANSFORM FOR REMOTE SENSING IMAGE EDGE DETECTION 被引量:1
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作者 Fu Wei Xing Guangzhong +2 位作者 Hou Lantian Qin Qiming Wang Wenjun 《Journal of Electronics(China)》 2006年第4期535-538,共4页
In the edge detection of Remote Sensing (RS) image, the useful detail losing and the spurious edge often appear. To solve the problem, the authors uses the dyadic wavelet to detect the edge of surface features by comb... In the edge detection of Remote Sensing (RS) image, the useful detail losing and the spurious edge often appear. To solve the problem, the authors uses the dyadic wavelet to detect the edge of surface features by combining the edge detecting with the multi-resolution analyzing of the wavelet transform. Via the dyadic wavelet decomposing, the RS image of a certain appropriate scale is obtained, and the edge data of the plane and the upright directions are respectively figured out, then the gradient vector module of the surface features is worked out. By tracing them, the authors get the edge data of the object, therefore build the RS image which obtains the checked edge. This method can depress the effect of noise and examine exactly the edge data of the object by rule and line. With an experiment of an RS image which obtains an airport, the authors certificate the feasibility of the application of dyadic wavelet in the object edge detection. 展开更多
关键词 Dyadic wavelet transform Edge detection and tracking of Remote Sensing (RS) Object recognition
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