To improve the capacity and imperceptibility of image steganography, a novel high capacity and imperceptibility image steganography method based on a combination of framelet and compressive sensing (CS) is put forward. Firstly, SVD (Singular Value Decomposition) transform to measurement values obtained by compressive sensing technique to the secret data. Then the singular values in turn embed into the low frequency coarse subbands of framelet transform to the blocks of the cover image which is divided into non-overlapping blocks. Finally, use inverse framelet transforms and combine to obtain the stego image. The experimental results show that the proposed steganography method has a good performance in hiding capacity, security and imperceptibility.
Compared with wavelet, framelet has good time frequency analysis ability and redundant characteristic. SVD (Singular Value Decomposition) can obtain stable feature of images which is not easily destroyed. To further improve the watermarking technique, a robust digital watermarking algorithm based on framelet and SVD is proposed. Firstly, Arnold transform is implemented to the grayscale watermark image. Secondly perform framelet transform to each host block which is divided according to the size of the watermark. Then embed the scrambled watermark into the biggest singular values produced in SVD transform to each coarse band gained from framelet transform to host image block. At last inverse framelet transform after inverse SVD transform to obtain embedded coarse band. Experimental results show that the proposed method gains good performance in robustness and security compared with traditional image processing including noise attack, cropping, filtering and JPEG compression etc. Moreover, the watermark imperceptibility of our method is better than that of wavelet and has stronger robustness than pure framelet without SVD.
A novel image fusion algorithm based on region segmentation and multiresolution analysis(MRA) is
proposed to make full use of advantages of different multiscale transform. Nonsubsampled
contourlet transform(NSCT) processes edges better than wavelet transform does. While wavelet
transform handles smooth area and singularities better than NSCT does. As an image often includes
more than one feature, the proposed method is conducted on the basis of Gaussian mixture
model(GMM) based region segmentation. Firstly, transform the multispectral(MS) image into
intensity, hue and saturation component. Secondly, segment intensity component into dense contour
and smooth regions according to GMM and NSCT. And then gain new intensity component by
fusing intensity component and high resolution image with Àtrous wavelet transform(ATWT) fusion
in smooth areas and NSCT fusion in dense contour areas. Finally transform the new intensity
together with hue component, saturation component back into RGB space and obtain the fused
image. Multisource remote sensing images are tested to assess this proposed algorithm. Visual
evaluation and statistics analysis are employed to evaluate the quality of fused images of different
methods. The proposed improved algorithm demonstrates excellent spectrum information and high
resolution. Experiment results show that the new proposed fusion algorithm incorporating with
region segmentation based improved GMM and MRA outperforms those algorithms based on
single multiscale transform.
Aiming at limitations of existing multiresolution analysis (MRA) fusion methods, this paper proposes a new fusion
method which combines curvelet and wavelet transform. Curvelet transform processes edges better than wavelet
transform does. While wavelet transform handles smooth area better than curvelet transform does. As an image often
includes more than one feature, the proposed method is conducted on the basis of region segmentation and use Àtrous
wavelet transform (ATWT) to fuse smooth areas and fast discrete curvelet transform (FDCT) to fuse areas with edges.
Furthermore, an optimal objective function defined based on a balance between spectral preservation and spatial
resolution improvement is put forward to search optimal segmentation threshold. The optimal fusion result can be
obtained by fusion processing through the optimal segmentation threshold. Landsat TM multispectral (MS) images and
SPOT Panchromatic (Pan) image covering a region of Wuhan in Hubei province are tested to assess this proposed
method. Visual evaluation and statistics analysis are employed to assess the quality of fused images of different methods.
The proposed method demonstrates best results among methods being tested in this study. So by combining attributes of
both transforms, it is possible to get better image fusion result than by using wavelet and curvelet individually.