Watermarking techniques are mainly used for protecting intellectual property right. This paper proposes a new hybrid nonblind video watermarking technique using **wavelet** **contourlet** **transform** and nonnegative matrix factorization **Wavelet** **transform** processed images are losing edge information. The **Contourlet** **transform** has good approximation properties for smooth 2D functions and finds a direct discrete space construction. But its performance is considered to be redundant. There evolved **wavelet** **based** **contourlet** **transform** (WBCT), as a nonredundant version of the **contourlet** **transform**. WBCT is used for watermarking video frames. The non negative matrix factorization (NMF) is used as dimension reduction technique in watermarking. NMF is applied to low pass and directional high pass sub bands which results from WBCT of each original video frame and gray scale watermark images. Embedding action is performed in low pass sub-band of WBCT processed video frame. The hybrid scheme improves the performance of watermarking scheme. The experimental results shows that the proposed video watermarking scheme provides better video processing operations such as cropping, rotation, histogram equalization ,compression, variety of noises , frame dropping, frame averaging and frame swapping and etc.

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This paper proposes the image fusion **based** on counterlet **transform** and discrete **wavelet** **transform**. The DWT and CT **transform** are used to extract the best features from different blur input images. The images are portioned **based** on dimensional reduction methods such as Laplacian pyramid and different coefficients from discrete **wavelet** **transform** to enhance the mean square error (MSE) and peak signal to noise ratio (PSNR) for exhibit the good appearance of output image i.e. image fusion. Hybrid DWT architecture has the advantage of lowers computational complexities and higher efficiencies. The algorithm is written in system MATLAB software. Image fusion **based** on contoulet **transform** and discrete **wavelet** **transform** gives better MSE and PSNR results as compared to existing methods.

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From a subjective point of view, we can clearly see that although the fusion results **based** on LP **transform** have certain brightness, the soft tissue information is ambiguous. Similarly, the soft tissue **based** on the fusion result of the literature [6] algorithm and the literature [4] algorithm. The performance of the information is relatively clear, but the image brightness is low and the resolution is low. The NSCT improves the brightness and improves the definition and contrast. However, the fusion result of the algorithm in this paper is superior in brightness and highlighting the target information, the details of the information also showed the ideal, good visual effects [15].

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Abstract: Image registration is a crucial step in most image processing tasks for which the final result is achieved from a combination of various resources. In general, the majority of registration methods consist of the following four steps: feature extraction, feature matching, **transform** modeling, and finally image resampling. As the accuracy of a registration process is highly dependent to the feature extraction and matching methods, in this paper, we have proposed a new method for extracting salient edges from satellite images. Due to the efficiency of multiresolution data representation, we have considered four state-of-the-art multiresolution transforms –namely, **wavelet**, curvelet, complex **wavelet** and **contourlet** **transform**- in the feature extraction step of the proposed image registration method. Experimental results and performance comparison among these transformations showed the high performance of the **contourlet** **transform** in extracting efficient edges from satellite images. Obtaining salient, stable and distinguishable features increased the accuracy of the proposed registration process.

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Abstract – Digital watermarking is an emerging technique to protect data security and intellectual property right. Identification or verification of watermarking patterns can be achieved by detecting watermarks in received signals. Watermarking is applied in the **contourlet** domain, which represents image edges sparsely, as the human visual system is less sensitive to the image edges. The **contourlet** **transform** is a new two-dimensional extension of the **wavelet** **transform** using multiscale and directional filter banks. In the presented scheme , watermark data is embedded in directional sub- band with the highest energy. By modeling the **contourlet** coefficients with General Gaussian Distribution (GGD), the distribution of watermarked noisy coefficients is analytically calculated. At the receiver, **based** on the Maximum Likelihood (ML) decision rule, an optimum detector by the aid of channel side information is proposed. In the next step, a blind extension of the suggested algorithm is presented using the patchwork idea.

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At present, most image retrieval algorithms use the underlying characteristics of the images to describe them, such as color, texture, appearance, etc. The main purpose of shoes image retrieval is to retrieve and return the shoes whose styles people are interested in. As shoes’ edge information is abundant, the texture features of shoes should be considered more. Texture features are usually obtained by statistical methods, structural methods, model method and frequency-domain method, Including co-occurrence matrix, Markov random field model, **wavelet** **transform**, etc[1]. In 2002, **based** on the **wavelet** multi-scale analysis, Do and Vetterli put forward **Contourlet** **Transform**[2], which is a new non-adaptive, directional and multi-scale analysis and can achieve decomposition in any direction and on any scale. It is good enough to describe the contours and direction of the image texture information in pictures, which makes up

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[2], [3] and Kolås et al. [4]. Random sprays have been partly inspired by the Human Visual System (HVS). In particular, a random spray is not dissimilar from the distribution of photo receptors in the retina, although the underlying mechanisms are vastly different. Due to the peaked nature of sprays, a common side effect of image enhancement methods that utilize spray sampling is the introduction of undesired noise in the output images. The magnitude and statistical characteristics of said noise are not known exactly because they depend on several factors such as image content, spray properties and algorithm parameters. Some of the most commonly used transforms for shrinkage **based** noise reduction are the **Wavelet** **Transform** (WT) [5]– [7], the Steerable Pyramid **Transform** [8]–[10], the **Contourlet** **Transform** [11]–[13] and the Shear let **Transform** [14]–[15]. With the exception of the WT, all other transforms lead to over-complete data representations. Over completeness is an important characteristic, as it is usually associated with the ability to distinguish data directionality in the **transform** space. We Independently of the specific **transform** used, the general assumption in multi-resolution shrinkage is that image data gives rise to sparse coefficients in the **transform** space. Thus, denoising can be achieved by shrinking those coefficients that compromise data sparsely. Such process is usually improved by an elaborate statistical analysis of the dependencies between coefficients at different scales. Yet, while effective, traditional multi-resolution methods are designed to only remove one particular type of noise (e.g. Gaussian noise). Furthermore, only the input image is assumed to be given. Due to the unknown statistical properties of the noise introduced by the use of sprays, traditional Approaches do not find the expected conditions, and thus their action becomes much.

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To advance proficient and viable image fusion algorithm, generous research work has been done as of late. Various combination components have been proposed by different creators to address the issues and difficulties of instatement for Multisensory picture combination, and to enhance the sensor combinations utilizing distinctive separating systems are examined in the writing [01]. In this article, the process of closeness measures, for example, Boundary **Based** Sameness Measurement and Structural Similarity Scale Measure is assessed and furthermore contrasted and the current therapeutic picture combination strategies. Immaterial and Performing: Multi methodology Medical Picture combination is the way toward melding two Surgical pictures acquired from two distinct detectors for best conclusion. This material advises a strategy for combination of Surgical pictures utilizing Dual Tree Complex **Wavelet** **Transform** (DTCWT) and Self Organizing Feature Map (SOFM). [02It spoke to Picture blend methodology of merging at least two image of same view to shape respective consolidated icon which demonstrates major

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of a set of face images. Turk and Pentland [3] have developed an automated system using Eigen faces with a similar concept to classify images in four different categories, which help to recognize true/false of positive of faces and build new set of image models. Use of Eigen spaces and Support Vector Machine for nighttime detection and classification of vehicles has been mentioned by Thi et al. [4]. S.Zehang, G.Bebis, and R.Miller [6] used PCA **based** vehicle classification framework. Harkirat S.Sahambi [7] and K.Khorasani used a neural network appearance **based** 3-D object recognition using Independent component analysis. N.G.Chitaliya and A.I.Trivedi [19] used **Wavelet**-PCA **based** feature extraction face recognition. Several multiscale geometric analysis (MGA) tools were proposed such as Curvelet [9, 10], bandlet and **Contourlet** [8, 11, 12, 14, 15] etc. Nonsubsampled **Contourlet** was pioneered by Do and Zhou as the latest MGA tool [11, 12], in 2005. **Contourlet** **transform** can effectively represent information than **wavelet** **transform** for the images having more directional information with smooth contour [18] due to its properties, viz. directionality and anisotropy. Yan et al. [16] proposed a faced recognition approach **based** on **Contourlet** **transform**. Yang et al. [13] proposed a multisensor image fusion method **based** on nonsubsampled **Contourlet** **transform**. N.G.Chitaliya and A.I.Trivedi [23, 24] used facial feature extraction using discrete **Contourlet** **transform** with Euclidean distance classifier and neural network.

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Wedgelets [2] use Haar functions on wedge partitions which is followed by the necessary shrinkage algorithm. Grouplets [15] and Easy Path **Wavelet** **Transform** [7], are **wavelet** **based** compression techniques which involve the definition of neighborhood by creating association fields and high correlation pathways for the sparse representation of two- dimensional data. Bandelet **transform** [14] is an adaptive technique which functions by image reduction along multiscale vectors which extend through the path of directional features. An image is reduced in a bandelet basis by employing fast subband filtering schemes. Methodologies such as Shearlets [12], Contourlets [10] and Curvelets [5] are non-adaptive image compression techniques. Shearlets [12], are a system of affine-like functions, that can be employed to represent any two dimensional functions which are separated by an amount of from the discontinuities all along the curves. **Contourlet** **transform** [10] is a 2D directional non- adaptive multi-resolution image representation scheme that confines the geometric edges of the image and its additional textural characteristics. The process includes the structuring of a discrete domain multi-resolution and multidirectional expansion by the usage of non-separable filter banks.

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In addition to the **wavelet** and the curvelet, we have the **contourlet** **transform** which appeared to overcome the limitations of the wavelets. It proposed an efficient direc- tional multiresolution image representation [18]. It has been developed by Do and Vetterli. The **contourlet** is **based** on an efficient 2D multiscale and directional filter that can deal effectively with images having smooth contours [21]. Therefore, it is able to capture contour and fine details in an image. Its approach starts with the dis- crete domain construction and then sparse expansion in the continuous domain [22]. In fact, **contourlet** **transform** can offer a sparse representation for piecewise smooth images [23].

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Many image fusion methods stated above assume the images to be non-noisy while fusing. But it is observed in many practical situa- tions that the images may be perturbed with some kinds of noises. Noise gets introduced due to poor lightning conditions while cap- turing medical images, satellite images like multispectral and hy- perspectral and other types of images. To remove the noise from the image, some methods preprocess the images before fusion and quite a few postprocess. But, it is observed that in either case, ef- ficiency of the fusion process degrades. In this paper, a simulata- neous denoising and fusion has been carried out; thus providing higher efficacy. Further both denoising and fusion process have been carried in the multiresolution framework where, image is de- composed into detail and approximate coefficients at various levels. In multiresolution denoising technique, an appropriate threshold to the MR **transform** coefficients is applied that suppress the effect of noise while preserving the edges and detail informations. Two such popular and widely used methods for thresholding are hard and soft thresholding. In hard thresholding technique, all the coef- ficients below the set threshold are rejected and reduced to zero. On the contrary, in soft thresholding such coefficients are reduced to zero by the magnitude of the threshold. Two popular techniques which make use of either soft and hard thresholding are discrete **wavelet** **transform** (DWT) [14], dual-tree complex **wavelet** trans- form (DT-CWT) [10] and **contourlet** **transform** (CT) [16]. In this paper multiresolution technique (MRT) technique has been employed to decompose the image into approximate and detail sub- bands at various scales. There have been a wide variety of strate- gies proposed in literature for effective denoising. The widely used methods include VisuShrink [12], SureShrink [8], BayesShrink [21] and NeighShrink [9]. In the proposed method soft thresholding has been applied at detail coefficients and nonlocal means filter [1] which is a popular non-linear filter is employed to the approximate coefficients for edge preserved denoising.

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Researchers developed a new filter bank **based** on non subsample **contourlet** and **wavelet** hybrid **transform** (NSCWHT) and study its application in [9]. They proposed a new image steganography **based** on developed NSCWHT. The work in [10] investigated the role of CT versus DWT in providing robust image steganography. Two measures are utilized in the comparison between **wavelet** **based** and **contourlet** **based** methods, peak signal to noise ratio (PSNR) and normalized cross correlation (NCC). A blind steganography algorithm is proposed in [6] in which extraction algorithm was designed as per maximum likelihood estimation. This paper carried on the static analysis to the high frequency subband coefficient of **wavelet** and **contourlet** **transform**. The extraction does not need original image neither does it need the original steganograph information.

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Glaucoma is one of the leading causes of blindness. It is caused by increased Intra Ocular Pressure (IOP) due to the malfunction of the drainage structure of the eyes. There are several methods to detect the Glaucoma from a human eye in the initial stages. In this paper a new system is proposed, which automatically detect Glaucoma disease in the human eye from the fundus database images. The feature extraction within images is **based** on **Contourlet** **Transform** (CT) and the classification is **based** on Support Vector Machine (SVM).In the existing system Discrete **Wavelet** **Transform** is used for feature extraction of Glaucoma images. Discrete **Wavelet** **Transform** have less number of information and the features obtained are only in three directions(vertical, horizontal, diagonal).Hence for better resolution and more directions **Contourlet** **Transform** is used. The better classification is arrived by extracting and selecting the best features from the **contourlet** coefficients of the fundus image and the outputs are used as an input to the Support Vector Machine classifier for classification with high accuracy. The proposed system classifies the input fundus images as normal or abnormal with very high accuracy.

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Singular Value Decomposition (SVD) deals withthe decomposition of general matrices which has proven to beuseful for numerous applications in science and engineeringdisciplines recently different techniques are used for compressing the images. Singular value decomposition is also recently used technique. In this paper we propose a method **based** on **contourlet** **wavelet** **transform** (CWT) and also the compression ratio also evaluated. Compared to SVD, H264, the contourlettransform provides accurate and effective results .The experimental results gives better performance and the method gives valid and accurate results .The implementation tool for the tests andexperiments is MATLAB.

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[Mallat.S, 1987] suggested that **wavelet** representation can be efficiently implemented with a pyramid architecture using quadrature mirror filters and the original signal can also be reconstructed with a similar architecture. The numerical stability was well illustrated by the quality of the reconstruction and the orientation selectivity of this representation which was useful for many applications. These applications of the **wavelet** representation include signal matching, data compression, edge detection, texture discrimination and fractal analysis. Computer vision applications have been emphasized and its representation can also be used for pattern recognition. [Mallat. S, et al., 1992] proposed singularity detection and processing of wavelets with fast oscillations. The local frequency of such oscillations is measured from the **Wavelet** **Transform** Modulus Maxima (WTMM). It has been shown numerically that one dimensional (1D) and two dimensional (2D) signals can be reconstructed with a good approximation; from the local maxima of their **wavelet** **transform** modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the **wavelet** **transform** maxima across scales. In 2D, the **wavelet** **transform** maxima indicate the location of edges in images and the denoising algorithm is extended for image enhancement.

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