摘要
提出了一种利用DCT变换和小波变换的特征层图像融合算法.其基本思想是先对多幅源图像进行分块DCT变换,选取较大方差对应的变换系数,将图像压缩为原图像大小的1/4,保留系数的对应坐标作为提取信息时的密钥;其次将经处理后的DCT系数直接作为小波变换的分解系数,经小波逆变换后得到融合信息.实验结果表明,该算法实现了多幅不同大小图像的融合,同时单一密钥只能提取单一图像.
A feature-level image fusion algorithm using wavelet transform combined with the discrete cosine transform （DCT） is proposed.The basic idea is to perform block-DCT of each source image first,and then to select the retained coefficients according to maximum variance;each image is compressed to 25 percent of the original size.The coordinates of the retained coefficients are used as private keys.Finally,the processed DCT coefficient matrixes are taken as the wavelet coefficients and the fused image is obtained by taking the inverse wavelet transform.The experimental results show that the algorithm realizes the fusion of images with different sizes.Furthermore,one single image can be reconstructed by one single private key.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2011年第11期301-307,共7页
Acta Physica Sinica
基金
江苏省高校自然科学基金(批准号:09KJA140002)
江苏省自然科学基金(批准号:BK2009400)资助的课题~~
