Sharif Digital Repository

A family of matrix embedding steganographic schemes for digital images is investigated. The target schemes are applied to blocks of n pixels in a cover image. In every block, at most m pixels are allowed to change with q-ary steps. We have derived some upper bounds on the embedding efficiency of these schemes for different values on m. It is also shown that these upper bounds approach the general upper bound on the embedding efficiency of q-ary steganography. For the case of q=3, we have shown that there is no feasible optimum member of the family for m=2, although for m=1, a well-known example exists. Instead, for m=2, a new close-to-bound scheme in the family is presented which exploits... 

Watermarking is a way that is used to protect the copyright of images. As the existing watermarking methods are not robust enough a new novel scheme is needed. In this paper, we propose a zero-watermark discrete cosine transformation-based copyright protection scheme, in which a normalization procedure is applied in both watermark embedding and extraction to make our scheme more robust to geometric distortions. This method is resistant to malicious signal processing and geometric attacks such as blurring, JPEG compression, noise adding, sharpening, scaling, rotation and cropping. Experimental results on images with different complexity show that our proposed scheme outperforms related... 

Noise reduction is a major concern in digital image processing systems, which have been investigated by several techniques. One kind of important noises in images is known as Salt & Pepper, which may have different causes. In this paper a new approach for eliminating this type of noise is presented. The approach is different from all previous works in this field, and is based on classification. More precisely, it tries to classify pixels of an image to one of two categories: noisy or not noisy. Afterward, it employs a median filter for pixels which have been classified as noisy, and remains others unchanged; hence, edges and other fine details are kept. Experimental results show this... 

This paper introduces two new compression methods. One of these methods is adaptive and powerful for the compression of hyperspectral data, which is based on separating the bands with different specifications by the histogram analyzes and new version of Binary Hybrid GA-PSO (BHGAPSO) and compressing each one with a different manner. The new proposed methods improve the compression ratio of the JPEG standards and save storage space the transmission system. The proposed methods are applied on different test cases, and the results are evaluated and compared with some other compression methods, such as lossless JPEG and JPEG2000  

Medical images include information about human body which are used for different purposes such as surgical and diagnostic plans. Compression of medical images is used in some applications such as profiling patient's data and transmission systems. Regard to importance of medical images information, lossless or near-lossless compression is preferred. Lossless JPEG, JPEG-LS and lossless version of JPEG2000 are few well-known methods for lossless compression. JPEG2000 is one of the latest and provides good compression ratio, however, it is complex [1]. In real time application which needs hardware implementation, low complex algorithm accelerates compression process. In this paper, a lossless... 

Clustering-based image segmentation is a well-known multispectral image segmentation method. However, as it inherently does not account for the spatial relation among image pixels, it often results in inhomogeneous segmented regions. The recently proposed membership-connectedness (MC)-based segmentation method considers the local and global spatial relations besides the fuzzy clustering stage to improve segmentation accuracy. However, the inherent spatial and intraclass redundancies in multispectral images might decrease the accuracy and efficiency of the method. This letter addresses these two problems and proposes a segmentation method that is based on the MC method, watershed transform,... 

We address blind extraction of spread spectrum data embedded in a digital image. The multicarrier iterative generalized least-squares (M-IGLS) data extraction algorithm is the basis of our study and a new algorithm based on M-IGLS is introduced in which both computational complexity and data extraction accuracy are enhanced. In the new method named multicarrier consecutive iterative generalized least-squares (M-CIGLS) algorithm, neither the cover image nor the spread spectrum carriers are required for the hidden data extraction. Additionally, knowledge of the number of carriers is no more needed. Simulation results show the ability of this method to extract data with a superior performance... 

The performance of degree of polarization (DOP) is investigated as a control signal in polarization-mode dispersion (PMD) compensation systems aided by polarization scrambling. The relation between the input and output polarization states of a signal propagating through a polarization scrambler and a PMD-induced optical fiber is described by a 3 × 3 Stokes transfer matrix. The average DOP of the output signal over a period of polarization scrambling is derived as an alternative to the conventional DOP-based control signal, i.e., minimum DOP. In the presence of first- and all-order PMDs, the performance of the average and minimum DOPs in monitoring of differential group delay (DGD) for... 

Fuzzy connectedness and fuzzy clustering are two well-known techniques for fuzzy image segmentation. The former considers the relation of pixels in the spatial space but does not inherently utilize their feature information. On the other hand, the latter does not consider the spatial relations among pixels. In this paper, a new segmentation algorithm is proposed in which these methods are combined via a notion called membership connectedness. In this algorithm, two kinds of local spatial attractions are considered in the functional form of membership connectedness and the required seeds can be selected automatically. The performance of the proposed method is evaluated using a developed... 

One of the most famous approaches for the segmentation of color images is finding clusters in the color space. Shapes of these clusters are often complex and the time complexity of the existing algorithms for finding clusters of different shapes is usually high. In this paper, a novel clustering algorithm is proposed and used for the image segmentation purpose. This algorithm distinguishes clusters of different shapes using a two-stage clustering approach in a reasonable time. In the first stage, the mean-shift clustering algorithm is used and the data points are grouped into some sub-clusters. In the second stage, connections between sub-clusters are established according to a dissimilarity... 

A Comprehensive speech database is one of the important tools for developing speech recognition systems; these tools are necessary for telephony recognition, too. Although adequate databases for direct speech recognizers exist, there is not an appropriate database for telephony speech recognizers. Most methods suggested for solving this problem are based on building new databases which tends to consume much time and many resources; or they used a filter which simulates circuit switch behavior to transform direct databases to telephony ones, in this case resulted databases have many differences with real telephony databases. In this paper we introduce a framework for creating telephony speech... 

This paper presents a complexity-based image compression method using neural networks. In this method, different multi-layer perceptron ANNs are used as compressor and de-compressor. Each image is divided into blocks, complexity of each block is computed using complexity measure methods and one network is selected for each block according to its complexity value. Three complexity measure methods, called Entropy, Activity and Pattern-based are used to determine the level of complexity in image blocks and their ability are evaluated and compared together. Selection of a network for each image block is based on its complexity value or the Best-SNR criterion. Best-SNR chooses one of the trained... 

In the image segmentation field, traditional techniques do not completely meet the segmentation challenges mostly posed by inherently fuzzy images. Fuzzy connectedness and fuzzy clustering are considered as two well-known techniques for introducing fuzzy concepts to the problem of image segmentation. Fuzzy connectedness-based Segmentation methods consider spatial relation of image pixels by "hanging togetherness" a notion based on intensity homogeneity. But, they do not inherently utilize feature information of image pixels. On the other hand, as the segmentation domain of fuzzy clustering-based methods is the feature space they do not consider spatial relations among image pixels. Recently,... 

Fusion of high spectral but low spatial resolution multispectral and low spectral but high spatial resolution panchromatic satellite images is a very useful technique in various remote sensing applications, such as change detection. Recently, some studies showed that wavelet-based image fusion methods provide high quality spectral content of the fused image. However, most of wavelet-based methods have a spatial resolution of the fused result less than the Brovey, IHS, and PCA fusion methods. This is mainly because real wavelet transform cannot efficiently represent the singularity of linear/curved contents. In this paper, we introduce a new method based on the contourlet transform which...