為了提高數(shù)字水印抗擊各種圖像攻擊的性能和保持圖像的穩(wěn)健性和不可見性，提出了一種基于離散小波變換（Dwt），SVD（singular value decomposition）奇異值分解水印圖像和原始載體圖像的離散余弦變換（DCT）的自適應(yīng)水印嵌入算法，主要是將水印圖像的兩次小波變換后的低頻分量潛入到原始圖像分塊經(jīng)過SVD分解的S分量矩陣中，同時(shí)根據(jù)圖像的JPEG壓縮比的不同計(jì)算各個(gè)圖像塊的水印調(diào)節(jié)因子。實(shí)驗(yàn)證明該算法在抗擊JPEG壓縮、中值濾波、加噪等均具有很好的魯棒性，嵌入后的圖像的PSNR達(dá)到38，具有良好的視覺掩蔽性
A series of .c and .m files which allow one to perform univariate and bivariate wavelet analysis of discrete time series. Noother wavelet package is necessary -- everything is contained in this archive. The C-code computes the Dwt and maximal overlap Dwt. MATLAB routines are then used to compute such quantities as the wavelet variance, covariance, correlation, cross-covariance and cross-correlation. Approximate confidence intervals are available for all quantities except the cross-covariance and cross-correlation.
A set of commands is provided. For a description of this example, please see http://www.eurandom.tue.nl/whitcher/software/.
-The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. Mutual Information is the similarity measure used in this case for optimizing the two images. This method requires estimating joint histogram of the two images. The fusion of images is the process of combining two or more images into a single image retaining important features from each. The Discrete Wavelet Transform (Dwt) has become an attractive tool for fusing multimodal images. In this work it has been used to segment the features of the input images to produce a region map. Features of each region are calculated and a region based approach is used to fuse the images in the wavelet domain.
