Characteristic Domain Method Of Blind Separation For Permuted Alias Images

Blind separation is new research direction in modern signal processing field, which has been applied to engineering. There is growing concern on single channel blind separation as frontier of blind separation research. However, in the applications such as image tamering etc, a special single channel alias mode called as permuted alias mode is encountered, which is different from the traditional superposition alias mode. Previous theory and method of single channel blind source separation are difficult to solve this new alias mode. This dissertation focuses on blind separation based characteristic domain method for permuted alias image.Information integrity of permuted alias image is losed for it is composed of different segement of source images with unoverlapping permution. Various sources of images make the permuted alias image diversity. At the same time, the position size and number of permuting image are unknown. The key of detecting and seperating permuting images is to find characteristic difference, which can be shown when permuted alias image is projected on a characteristic domain. This dissertation proposes methods of blind separation in various characteristic domain for various type of permuted alias images. The contributions of the dissertation are summarized as follows.1. A blind separation method based sparse decomposing is presented according to a type of permuted alias image, when morphological diversity exists betweeen permuted and permuting region of image, which can be expressed sparsity diversity decomposed on different dictionaries. Sparsity representation model of permuted alias image is obtained by its mathematical model. On the base of characteristics of permuting and permuted region image, Contourlet and Local DCT base dictionary are chosen as characteristic domain. Sparse decomposed iterately on two different charactersitic domains, getting sparsity representaion of different morphological images by block coordinate relaxation method, the permuted image can be seperated from the permuted alias image. Results show that, as for a permuted image comprising piecewise smoooth part and texture part, our algorithm can better separate texture image from the permuted image, not affected by size, location, number and types of texture image.2. Different region of permuted alias image have noise difference, for permuting and permuted images come from different source images and they usually be infected by noise in the process of acquisition, transmission and processing. An algorithm about permuted alias image blind separation based noise detection is proposed according to a class of permuted alias image with noise difference. Learning dictionary is chosen as characteristic domain, for it has high adaptability and its sparsity representation has high sparsity degrees than that of parameter dictionary. Permuted alias image is denoised by getting its sparsity representation with K-SVD restrained by nonzeros number dictionary learning algorithm with itself smapling. Size and location of permuting region is found out by detecting the subtraction image, which is defined as difference between the denoised permuted alias image and original permuted alias image. The permuting region is optimized by implementing image morphological operation and is separated from the permuted alias image by setting threshold. The results show that the region noised can be effectively denoised only with two constant nonzeros number and permuting image can be separated efficiently from the permuted alias image, not affected by size, location, number of permuting image and noise level on permuting image.3. A blind separation algorithm based on four-direction-difference is proposed for a type of permuted alias image with motion blur difference. Firstly, space domain is chosen as characteristic domain after analysing characteristics of motion blur image. Four differential images in four directions are obtained by four direction operating of permuted alias image. A binary image can be obtained by calculating variance value of each differencd image subblock and thresholding it, to detect rough location of permuting image. Permuting images can be seperated from permuted alias image by multiplying binary image which is optimized with image morphological operation. The results show that blurry images can be effectively separated from the permuted alias image without respect to the location, size and number of motion blur image and direction of motion blur.4. A blind separation algorithm based on differential evolution is proposed for a type of permuted alias image with gaussian blur difference. Firstly, space domain is chosen as characteristic domain after analysing characteristics of gaussian blur image. Differential image of permuted alias image is divided into sub-blocks which is randomly assigned a threshold and all the thresholds formed a threshold vector. The differential evolution is then performed to obtain the optimal threshold vector for thresholding the differential image into binary one. The permuting image could be separated by permuted alias image multiplid by the binary image optimized with image morphology operating. Experimental results show that the proposed approach could effectively separate the permuting image from the permuted alias image without respect to the location, size and nemuber of permuting image.