reversible watermark using the difference expansion of a generalized integer transform a reversible watermarking algorithm with very high data hiding capacity has been developed for color images the algorithm allows the watermarking process to be reversed which restores the exact original image the algorithm hides several bits in the difference expansion of vectors of adjacent pixels the required general reversible integer transform and the necessary conditions to avoid underflow and overflow are derived for any vector of arbitrary length also the potential payload size that can be embedded into a host image is discussed and a feedback system for controlling this size is developed in addition to maximize the amount of data that can be hidden into an image the embedding algorithm can be applied recursively across the color components simulation results using spatial triplets spatial quads cross color triplets and cross color quads are presented and compared with the existing reversible watermarking algorithms these results indicate that the spatial quad based algorithm allows for hiding the largest payload at the highest signal to noise ratio

a high payload frequency based reversible image hiding method to provide both a high payload capacity and a good stego image quality a high payload frequency based reversible image hiding hpfrih method was presented in this paper these performances can be achieved by transforming a cover image from the spatial domain into the frequency domain through haar digital wavelet transform hdwt method followed by an adaptive arithmetic coding method to encode the hdwt coefficients in a high frequency band the hpfrih method was used to conceal both the compressed data and the secret data in the high frequency band this simple method can completely reconstruct the cover image only from the stego image moreover experimental results showed that the hpfrih method offers both impressive hiding capacity and satisfied stego image quality

nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising part ii adaptive algorithms we combine the main ideas introduced in part i with adaptive techniques to arrive at a powerful algorithm that estimates missing data in nonstationary signals the proposed approach operates automatically based on a chosen linear transform that is expected to provide sparse decompositions over missing regions such that a portion of the transform coefficients over missing regions are zero or close to zero unlike prevalent algorithms our method does not necessitate any complex preconditioning segmentation or edge detection steps and it can be written as a progression of denoising operations we show that constructing estimates based on nonlinear approximants is fundamentally a nonconvex problem and we propose a progressive algorithm that is designed to deal with this issue directly the algorithm is applied to images through an extensive set of simulation examples primarily on missing regions containing textures edges and other image features that are not readily handled by established estimation and recovery methods we discuss the properties required of good transforms and in conjunction show the types of regions over which well known transforms provide good predictors we further discuss extensions of the algorithm where the utilized transforms are also chosen adaptively where unpredictable signal components in the progressions are identified and not predicted and where the prediction scenario is more general

missing texture reconstruction method based on error reduction algorithm using fourier transform magnitude estimation scheme a missing texture reconstruction method based on an error reduction er algorithm including a novel estimation scheme of fourier transform magnitudes is presented in this brief in our method fourier transform magnitude is estimated for a target patch including missing areas and the missing intensities are estimated by retrieving its phase based on the er algorithm specifically by monitoring errors converged in the er algorithm known patches whose fourier transform magnitudes are similar to that of the target patch are selected from the target image in the second approach the fourier transform magnitude of the target patch is estimated from those of the selected known patches and their corresponding errors consequently by using the er algorithm we can estimate both the fourier transform magnitudes and phases to reconstruct the missing areas

a group based image inpainting using patch refinement in mrf framework this paper presents a markov random field mrf based image inpainting algorithm using patch selection from groups of similar patches and optimal patch assignment through joint patch refinement in patch selection a novel group formation strategy based on subspace clustering is introduced to search the candidate patches in relevant source region only this improves patch searching in terms of both quality and time we also propose an efficient patch refinement scheme using higher order singular value decomposition to capture underlying pattern among the candidate patches this eliminates random variation and unwanted artifacts as well finally a weight term is computed based on the refined patches and is incorporated in the objective function of the mrf model to improve the optimal patch assignment experimental results on a large number of natural images and comparison with well known existing methods demonstrate the efficacy and superiority of the proposed method

data embedding for vector quantization image processing on the basis of adjoining state codebook mapping information hiding for images is an important research issue in digital multimedia it provides a way to certify copyrights of image files as well as a means of secret communication for people in this paper we propose a reversible image information hiding scheme based on vector quantization vq the proposed scheme uses a new technique called adjoining state codebook mapping ascm to map the content of an image block to an index in the corresponding state codebooks the ascm technique uses two adjacent image blocks of an encoding image block to create two state codebooks which can represent the encoding image block more accurately therefore it can reduce the size of the output code stream and yet still provide large embedding capacity for secret data experimental results indicate that the proposed scheme has better compression bit rate and larger embedding capacity when we compare the proposed scheme with recent similar studies proposed by chang et al in 2009 and yang et al in 2011 respectively

reversible data hiding based on block median preservation this paper proposes a reversible data hiding scheme for gray level images it exploits the high correlation among image block pixels to produce a difference histogram secret data is embedded based on a multi level histogram shifting mechanism with reference to the integer median of each block the image blocks are divided into four categories due to four corresponding embedding strategies aiming at preserving the medians during data embedding in decoder the median pixels are retrieved first followed by the hidden data extraction and the host image can be accurately recovered via an inverse histogram shifting mechanism after removing the secret data from the marked image experimental results validate the effectiveness of our scheme and demonstrate that it outperforms several previous methods in terms of capacity and marked image s quality

lsb steganographic method based on reversible histogram transformation function for resisting statistical steganalysis statistical steganalysis schemes detect the existence of secret information embedded by steganography the x 2 detection and regular singular rs attack methods are two well known statistical steganalysis schemes used against lsb least significant bit steganography the embedded message length can be estimated accurately by these two steganalysis schemes for secret communication the resistance of steganography against steganalysis is very important for information security to avoid the enemy s attempts the statistical features between stego images and cover images should be as similar as possible for better resistance to steganalysis in this manuscript a reversible histogram transformation function based lsb steganographic method is proposed to resist statistical steganalysis the experimental results show that the proposed method resists not only rs attack but also x 2 detection methods

a high capacity reversible watermarking approach for authenticating images exploiting down sampling histogram processing and block selection amongst the various data hiding schemes lossless data hiding has achieved substantial attention in the recent years lossless watermarking also known as reversible watermarking not only ensures the extraction of the hidden information called watermark but also the complete recovery of the original image as well recently several interesting reversible block based watermarking approaches using histogram processing have been reported these approaches offer substantial watermarking capacity for a fixed image quality measure however there is considerable margin of improvement as regards the imperceptibility versus capacity tradeoff is concerned some of the watermarking applications such as those related to integrity control need high capacity therefore we present a high capacity reversible watermarking approach based on histogram processing and block selection rw hpbs the proposed rw hpbs approach exploits the concept of down sampling for effectively increasing the capacity and is useful for integrity control and authentication related applications two down sampled versions the reference and the data hiding are obtained to create space for embedding authentication property is achieved by taking a secondary advantage of the location map before being compressed and embedded as an auxiliary information the effectiveness of the proposed method is shown through comparison with the existing techniques experimental results show that the proposed rw hpbs approach provides better imperceptibility versus capacity tradeoff and can detect tampering and collage attack

the contourlet transform an efficient directional multiresolution image representation the limitations of commonly used separable extensions of one dimensional transforms such as the fourier and wavelet transforms in capturing the geometry of image edges are well known in this paper we pursue a true two dimensional transform that can capture the intrinsic geometrical structure that is key in visual information the main challenge in exploring geometry in images comes from the discrete nature of the data thus unlike other approaches such as curvelets that first develop a transform in the continuous domain and then discretize for sampled data our approach starts with a discrete domain construction and then studies its convergence to an expansion in the continuous domain specifically we construct a discrete domain multiresolution and multidirection expansion using nonseparable filter banks in much the same way that wavelets were derived from filter banks this construction results in a flexible multiresolution local and directional image expansion using contour segments and thus it is named the contourlet transform the discrete contourlet transform has a fast iterated filter bank algorithm that requires an order n operations for n pixel images furthermore we establish a precise link between the developed filter bank and the associated continuous domain contourlet expansion via a directional multiresolution analysis framework we show that with parabolic scaling and sufficient directional vanishing moments contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves finally we show some numerical experiments demonstrating the potential of contourlets in several image processing applications

adaptive lossless steganographic scheme with centralized difference expansion in this paper a novel adaptive lossless data hiding scheme is presented that is capable of offering greater embedding capacity than the existing schemes unlike the fixed hiding capacity each block provides in most of the currently available lossless data hiding approaches the proposed method utilizes a block based lossless data embedding algorithm where the quantity of the hidden information each block bears is variable to both reduce the image distortion and increase the hiding capacity the payload of each block depends on its cover image complexity due to the fact that schemes with difference expansion tend to damage the image quality seriously in the edge areas in the proposed scheme smoother areas are chosen to conceal more secret bits this way a better balance can be reached between the embedding ratio and the stego image quality in addition when recovered the cover image can came back to its old self to the last bit without any distortion at all experimental results as this paper will show have demonstrated that the proposed method is capable of hiding more secret data while maintaining imperceptible stego image quality degradation

smooth parafac decomposition for tensor completion in recent years low rank based tensor completion which is a higher order extension of matrix completion has received considerable attention however the low rank assumption is not sufficient for the recovery of visual data such as color and 3d images when the ratio of missing data is extremely high in this paper we consider smoothness constraints as well as low rank approximations and propose an efficient algorithm for performing tensor completion that is particularly powerful regarding visual data the proposed method admits significant advantages owing to the integration of smooth parafac decomposition for incomplete tensors and the efficient selection of models in order to minimize the tensor rank thus our proposed method is termed as smooth parafac tensor completion spc in order to impose the smoothness constraints we employ two strategies total variation spc tv and quadratic variation spc qv and invoke the corresponding algorithms for model learning extensive experimental evaluations on both synthetic and real world visual data illustrate the significant improvements of our method in terms of both prediction performance and efficiency compared with many state of the art tensor completion methods

a new data hiding scheme for binary image authentication with small image distortion a new data hiding scheme for binary image authentication that has a small distortion of the cover image is proposed in this paper using the data embedding algorithm that is based on hamming codes the proposed scheme embeds authentication information into the cover image with flipping only a small number of pixels a special type of the pixels are selected and flipped by a new algorithm to minimize visual distortion this new algorithm is based on elssm edge line segment similarity measure randomly shuffling the bit order of the authentication information to be embedded the information can only be extracted by the designated receiver who has the symmetric key we employ two measurement metrics miss detection rates for the degree of security and psnr peak signal to noise ratio and elssm for the degree of the image distortion to demonstrate the feasibility of the proposed scheme using these metrics we analyze the proposed scheme and the previous schemes the analysis reveals that the proposed scheme requires less image distortion than the previous schemes whilst achieving the same level of the miss detection rate experimental results demonstrate that the proposed scheme is more resilient against recent steganalysis attacks than the previous schemes

structure based low rank model with graph nuclear norm regularization for noise removal nonlocal image representation methods including group based sparse coding and block matching 3 d filtering have shown their great performance in application to low level tasks the nonlocal prior is extracted from each group consisting of patches with similar intensities grouping patches based on intensity similarity however gives rise to disturbance and inaccuracy in estimation of the true images to address this problem we propose a structure based low rank model with graph nuclear norm regularization we exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure with the manifold structure information a graph nuclear norm regularization is established and incorporated into a low rank approximation model we then prove that the graph based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular value thresholding algorithm extensive experiments on additive white gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state of the art algorithms

image denoising via sparse and redundant representations over learned dictionaries we address the image denoising problem where zero mean white and homogeneous gaussian additive noise is to be removed from a given image the approach taken is based on sparse and redundant representations over trained dictionaries using the k svd algorithm we obtain a dictionary that describes the image content effectively two training options are considered using the corrupted image itself or training on a corpus of high quality image database since the k svd is limited in handling small image patches we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image we show how such bayesian treatment leads to a simple and effective denoising algorithm this leads to a state of the art denoising performance equivalent and sometimes surpassing recently published leading alternative denoising methods

multilevel reversible data hiding based on histogram modification of difference images reversible data hiding has drawn considerable attention in recent years reversibility allows original media to be completely recovered from marked media without distortion after embedded message has been extracted in this paper we propose a multilevel reversible data hiding scheme based on the difference image histogram modification that uses the peak point to hide messages through a joint imperceptibility and hiding capacity evaluation we show that our proposed scheme uses a multilevel hiding strategy to achieve large hiding capacity and keep distortion low performance comparisons with other existing reversible hiding schemes are provided to demonstrate the validity of our proposed scheme

reversible data hiding using delaunay triangulation and selective embedment in this paper we propose a reversible data hiding method based on prediction and histogram shifting phs using delaunay triangulation and selective embedment because the statistical properties of the prediction errors along the embedding direction are significantly altered due to the shifting operation the existing phs based methods are vulnerable to some steganalyzers the proposed method exploits a set of key selected referential pixels to construct a 3d delaunay mesh to obtain the prediction errors the presence of the altered statistical properties along the embedding direction is unrevealed because the same set of prediction errors cannot be reconstructed a selective embedment mechanism is used to control the embedding regions in the cover image for evading the detection of the steganalyzers based on low amplitude stego signal the experimental results reveal that the proposed method not only provides better payload and image quality than the existing phs based methods but is also robust to the detection of modern steganalyzers such as histogram analysis and spam

annihilating filter based low rank hankel matrix approach for image inpainting in this paper we propose a patch based image inpainting method using a low rank hankel structured matrix completion approach the proposed method exploits the annihilation property between a shift invariant filter and image data observed in many existing inpainting algorithms in particular by exploiting the commutative property of the convolution the annihilation property results in a low rank block hankel structure data matrix and the image inpainting problem becomes a low rank structured matrix completion problem the block hankel structured matrices are obtained patch by patch to adapt to the local changes in the image statistics to solve the structured low rank matrix completion problem we employ an alternating direction method of multipliers with factorization matrix initialization using the low rank matrix fitting algorithm as a side product of the matrix factorization locally adaptive dictionaries can be also easily constructed despite the simplicity of the algorithm the experimental results using irregularly subsampled images as well as various images with globally missing patterns showed that the proposed method outperforms existing state of the art image inpainting methods

patch ordering based wavelet frame and its use in inverse problems in our previous work 1 we have introduced a redundant tree based wavelet transform rtbwt originally designed to represent functions defined on high dimensional data clouds and graphs we have further shown that rtbwt can be used as a highly effective image adaptive redundant transform that operates on an image using orderings of its overlapped patches the resulting transform is robust to corruptions in the image and thus able to efficiently represent the unknown target image even when it is calculated from its corrupted version in this paper we utilize this redundant transform as a powerful sparsity promoting regularizer in inverse problems in image processing we show that the image representation obtained with this transform is a frame expansion and derive the analysis and synthesis operators associated with it we explore the use of this frame operators to image denoising and deblurring and demonstrate in both these cases state of the art results

adaptive image denoising by targeted databases we propose a data dependent denoising procedure to restore noisy images different from existing denoising algorithms which search for patches from either the noisy image or a generic database the new algorithm finds patches from a database that contains relevant patches we formulate the denoising problem as an optimal filter design problem and make two contributions first we determine the basis function of the denoising filter by solving a group sparsity minimization problem the optimization formulation generalizes existing denoising algorithms and offers systematic analysis of the performance improvement methods are proposed to enhance the patch search process second we determine the spectral coefficients of the denoising filter by considering a localized bayesian prior the localized prior leverages the similarity of the targeted database alleviates the intensive bayesian computation and links the new method to the classical linear minimum mean squared error estimation we demonstrate applications of the proposed method in a variety of scenarios including text images multiview images and face images experimental results show the superiority of the new algorithm over existing methods

image inpainting by patch propagation using patch sparsity this paper introduces a novel examplar based inpainting algorithm through investigating the sparsity of natural image patches two novel concepts of sparsity at the patch level are proposed for modeling the patch priority and patch representation which are two crucial steps for patch propagation in the examplar based inpainting approach first patch structure sparsity is designed to measure the confidence of a patch located at the image structure e g the edge or corner by the sparseness of its nonzero similarities to the neighboring patches the patch with larger structure sparsity will be assigned higher priority for further inpainting second it is assumed that the patch to be filled can be represented by the sparse linear combination of candidate patches under the local patch consistency constraint in a framework of sparse representation compared with the traditional examplar based inpainting approach structure sparsity enables better discrimination of structure and texture and the patch sparse representation forces the newly inpainted regions to be sharp and consistent with the surrounding textures experiments on synthetic and natural images show the advantages of the proposed approach

restoration by compression in this paper we study the topic of signal restoration using complexity regularization quantifying the compression bit cost of the signal estimate while complexity regularized restoration is an established concept solid practical methods were suggested only for the gaussian denoising task leaving more complicated restoration problems without a generally constructive approach here we present practical methods for complexity regularized restoration of signals accommodating deteriorations caused by a known linear degradation operator of an arbitrary form our iterative procedure obtained using the alternating direction method of multipliers admm approach addresses the restoration task as a sequence of simpler problems involving ell 2 regularized estimations and rate distortion optimizations considering the squared error criterion we replace the rate distortion optimizations with an arbitrary standardized compression technique and thereby restore the signal by leveraging underlying models designed for compression additionally we propose a shift invariant complexity regularizer measuring the bit cost of all the shifted forms of the estimate extending our method to use averaging of decompressed outputs gathered from compression of shifted signals on the theoretical side we present an analysis of complexity regularized restoration of a cyclo stationary gaussian signal from deterioration by a linear shift invariant operator and an additive white gaussian noise the theory shows that optimal complexity regularized restoration relies on an elementary restoration filter and compression spreading reconstruction quality unevenly based on the energy distribution of the degradation filter these ideas are realized also in the proposed practical methods finally we present experiments showing good results for image deblurring and inpainting using the jpeg2000 and hevc compression standards

lossless data hiding for color images based on block truncation coding in this paper we present a novel reversible steganographic method which can reconstruct an original image effectively after extracting the embedded secret data the proposed reversible hiding method aims at btc block truncation coding compressed color images conventionally each block of a color image compressed by btc requires three bitmaps and three pairs of quantization levels for reconstruction in order to improve the compression rate a genetic algorithm ga is applied to find an approximate optimal common bitmap to replace the original three the secret data then are embedded in the common bitmap and the quantization levels of each block use the properties of side matching and the order of these quantization levels to achieve reversibility the experimental results demonstrate that the proposed method is practical for btc compressed color images and can embed more than three bits in each btc encoded block on average

legclust a clustering algorithm based on layered entropic subgraphs hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set the most common proximity measures are distance measures the derived proximity matrices can be used to build graphs which provide the basic structure for some clustering methods we present here a new proximity matrix based on an entropic measure and also a clustering algorithm legciust that builds layers of subgraphs based on this matrix and uses them and a hierarchical agglomerative clustering technique to form the clusters our approach capitalizes on both a graph structure and a hierarchical construction moreover by using entropy as a proximity measure we are able with no assumption about the cluster shapes to capture the local structure of the data forcing the clustering method to reflect this structure we present several experiments on artificial and real data sets that provide evidence on the superior performance of this new algorithm when compared with competing ones

a decoupled method for image inpainting with patch based low rank regulariztion abstract in this paper we propose a decoupled variational method for image inpainting in both image domain and transform domain including wavelet domain and fourier domain the original image inpainting problem is decoupled as two minimization problems with different energy functionals one is image denoising with low rank regularization method i e the patch based weighted nuclear norm minimization pwnnm the other is linear combination in image domain or transform domain an iterative algorithm is then obtained by minimizing the two problems alternatingly in particular we derive the variational formulas for pwnnm and reformulate the denoising process into three steps image decomposition patch matrix denoising and image reconstruction the convergence of the numerical algorithm is proved under some assumptions the numerical experiments and comparisons on various images demonstrate the effectiveness of the proposed methods

the curvelet transform for image denoising we describe approximate digital implementations of two new mathematical transforms namely the ridgelet transform and the curvelet transform our implementations offer exact reconstruction stability against perturbations ease of implementation and low computational complexity a central tool is fourier domain computation of an approximate digital radon transform we introduce a very simple interpolation in the fourier space which takes cartesian samples and yields samples on a rectopolar grid which is a pseudo polar sampling set based on a concentric squares geometry despite the crudeness of our interpolation the visual performance is surprisingly good our ridgelet transform applies to the radon transform a special overcomplete wavelet pyramid whose wavelets have compact support in the frequency domain our curvelet transform uses our ridgelet transform as a component step and implements curvelet subbands using a filter bank of a spl grave trous wavelet filters our philosophy throughout is that transforms should be overcomplete rather than critically sampled we apply these digital transforms to the denoising of some standard images embedded in white noise in the tests reported here simple thresholding of the curvelet coefficients is very competitive with state of the art techniques based on wavelets including thresholding of decimated or undecimated wavelet transforms and also including tree based bayesian posterior mean methods moreover the curvelet reconstructions exhibit higher perceptual quality than wavelet based reconstructions offering visually sharper images and in particular higher quality recovery of edges and of faint linear and curvilinear features existing theory for curvelet and ridgelet transforms suggests that these new approaches can outperform wavelet methods in certain image reconstruction problems the empirical results reported here are in encouraging agreement

rotation invariant watermark embedding based on scale adapted characteristic regions rotation invariance is one of the most challenging issues in robust image watermarking this paper presents two rotation invariant watermark embedding schemes in the non subsampled contourlet transform nsct domain based on the scale adapted local regions watermark synchronization is achieved using the scale space feature point based local characteristic regions the first method embeds a binary watermark sequence by partitioning the local regions in a rotation invariant pattern the second method embeds a binary watermark image in a content based manner and the watermark signal is adaptive to the orientation of the image both methods achieve rotation invariant embedding by only using the rotation normalizing angle thus no interpolation operation is performed extensive simulation results and comparisons show that the proposed schemes can efficiently resist both signal processing attacks and geometric attacks

information theoretic measures for clusterings comparison variants properties normalization and correction for chance information theoretic measures form a fundamental class of measures for comparing clusterings and have recently received increasing interest nevertheless a number of questions concerning their properties and inter relationships remain unresolved in this paper we perform an organized study of information theoretic measures for clustering comparison including several existing popular measures in the literature as well as some newly proposed ones we discuss and prove their important properties such as the metric property and the normalization property we then highlight to the clustering community the importance of correcting information theoretic measures for chance especially when the data size is small compared to the number of clusters present therein of the available information theoretic based measures we advocate the normalized information distance nid as a general measure of choice for it possesses concurrently several important properties such as being both a metric and a normalized measure admitting an exact analytical adjusted for chance form and using the nominal 0 1 range better than other normalized variants

denpehc density peak based efficient hierarchical clustering abstract existing hierarchical clustering algorithms involve a flat clustering component and an additional agglomerative or divisive procedure this paper presents a density peak based hierarchical clustering method denpehc which directly generates clusters on each possible clustering layer and introduces a grid granulation framework to enable denpehc to cluster large scale and high dimensional lshd datasets this study consists of three parts 1 utilizing the distribution of the parameter which is defined as the product of the local density and the minimal distance to data points with higher density in clustering by fast search and find of density peaks dpclust and a linear fitting approach to select clustering centers with the clustering hierarchy decided by finding the stairs in the curve 2 analyzing the leading tree in which each node except the root is led by its parent to join the same cluster as an intermediate result of dpclust and constructing the clustering hierarchy efficiently based on the tree and 3 designing a framework to enable denpehc to cluster lshd datasets when a large number of attributes can be grouped by their semantics the proposed method builds the clustering hierarchy by simply disconnecting the center points from their parents with a linear computational complexity o m where m is the number of clusters experiments on synthetic and real datasets show that the proposed method has promising efficiency accuracy and robustness compared to state of the art methods

hierarchical super resolution based inpainting this paper introduces a novel framework for examplar based inpainting it consists in performing first the inpainting on a coarse version of the input image a hierarchical super resolution algorithm is then used to recover details on the missing areas the advantage of this approach is that it is easier to inpaint low resolution pictures than high resolution ones the gain is both in terms of computational complexity and visual quality however to be less sensitive to the parameter setting of the inpainting method the low resolution input picture is inpainted several times with different configurations results are efficiently combined with a loopy belief propagation and details are recovered by a single image super resolution algorithm experimental results in a context of image editing and texture synthesis demonstrate the effectiveness of the proposed method results are compared to five state of the art inpainting methods

solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity a general framework for solving image inverse problems with piecewise linear estimations is introduced in this paper the approach is based on gaussian mixture models which are estimated via a maximum a posteriori expectation maximization algorithm a dual mathematical interpretation of the proposed framework with a structured sparse estimation is described which shows that the resulting piecewise linear estimate stabilizes the estimation when compared with traditional sparse inverse problem techniques we demonstrate that in a number of image inverse problems including interpolation zooming and deblurring of narrow kernels the same simple and computationally efficient algorithm yields results in the same ballpark as that of the state of the art

nonlocal image restoration with bilateral variance estimation a low rank approach simultaneous sparse coding ssc or nonlocal image representation has shown great potential in various low level vision tasks leading to several state of the art image restoration techniques including bm3d and lssc however it still lacks a physically plausible explanation about why ssc is a better model than conventional sparse coding for the class of natural images meanwhile the problem of sparsity optimization especially when tangled with dictionary learning is computationally difficult to solve in this paper we take a low rank approach toward ssc and provide a conceptually simple interpretation from a bilateral variance estimation perspective namely that singular value decomposition of similar packed patches can be viewed as pooling both local and nonlocal information for estimating signal variances such perspective inspires us to develop a new class of image restoration algorithms called spatially adaptive iterative singular value thresholding saist for noise data saist generalizes the celebrated bayesshrink from local to nonlocal models for incomplete data saist extends previous deterministic annealing based solution to sparsity optimization through incorporating the idea of dictionary learning in addition to conceptual simplicity and computational efficiency saist has achieved highly competent often better objective performance compared to several state of the art methods in image denoising and completion experiments our subjective quality results compare favorably with those obtained by existing techniques especially at high noise levels and with a large amount of missing data

a reversible data hiding scheme using complementary embedding strategy obtaining good visual quality and high hiding capacity with reversible data hiding systems is a technically challenging problem in this paper we propose a simple reversible data hiding scheme that uses a complementary hiding strategy the proposed method embeds one secret bit horizontally and vertically into one cover pixel of a grayscale cover image by decreasing odd valued pixels and increasing even valued pixels by one experimental results show that the hiding capacity measured by bit per pixel bpp of the proposed scheme is at least 1 21bpp with a psnr peak signal to noise ratio value greater than 52db for all standard test images especially in the case of four layer embedding the psnr value of the proposed method is still greater than 51db at a hiding capacity of about 5bpp for all standard test images in addition the proposed method is quite simple because it primarily uses additions and subtractions these results indicate that the proposed scheme is superior to many existing reversible data hiding schemes introduced in the literature

fast approximate energy minimization via graph cuts many tasks in computer vision involve assigning a label such as disparity to every pixel a common constraint is that the labels should vary smoothly almost everywhere while preserving sharp discontinuities that may exist e g at object boundaries these tasks are naturally stated in terms of energy minimization the authors consider a wide class of energies with various smoothness constraints global minimization of these energy functions is np hard even in the simplest discontinuity preserving case therefore our focus is on efficient approximation algorithms we present two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves namely expansion moves and swap moves these moves can simultaneously change the labels of arbitrarily large sets of pixels in contrast many standard algorithms including simulated annealing use small moves where only one pixel changes its label at a time our expansion algorithm finds a labeling within a known factor of the global minimum while our swap algorithm handles more general energy functions both of these algorithms allow important cases of discontinuity preserving energies we experimentally demonstrate the effectiveness of our approach for image restoration stereo and motion on real data with ground truth we achieve 98 percent accuracy

hierarchical clustering problems and analysis of fuzzy proximity relation on granular space in this paper based on granular space some hierarchical clustering problems and analysis for fuzzy proximity relation are developed by using rigorous mathematical descriptions and four results are obtained first the granular representation of a fuzzy proximity relation is studied an algorithm is obtained to compute the granular space or hierarchical clustering structure and the min transitive closure derived from the same fuzzy proximity relation on a finite universe and the consistent clustering properties are discussed second a consistency index that is based on granular space is developed and a global optimizing mathematical model is established to obtain the optimal clustering of a fuzzy proximity relation furthermore for given two fuzzy proximity relations on different granulations of the same universe a collaborative clustering method is studied by the intersection operation and this method can provide a better hierarchical clustering structure finally the theory for hierarchical clustering analysis of fuzzy proximity relations on granular space is established for given two fuzzy proximity relations on the same universe two sufficient conditions theorems 6 1 and 6 2 of isomorphism are given and the necessary condition theorem 6 3 and the sufficient condition theorem 6 4 for e similarity are obtained we also illustrate the effectiveness of these theories and methods with some practical examples these results may help form a comprehensive theory and methodology for related potential application and will help us to obtain a deeper understanding of the essence of hierarchical clustering procedures

nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising part i theory we study the robust estimation of missing regions in images and video using adaptive sparse reconstructions our primary application is on missing regions of pixels containing textures edges and other image features that are not readily handled by prevalent estimation and recovery algorithms we assume that we are given a linear transform that is expected to provide sparse decompositions over missing regions such that a portion of the transform coefficients over missing regions are zero or close to zero we adaptively determine these small magnitude coefficients through thresholding establish sparsity constraints and estimate missing regions in images using information surrounding these regions unlike prevalent algorithms our approach does not necessitate any complex preconditioning segmentation or edge detection steps and it can be written as a sequence of denoising operations we show that the region types we can effectively estimate in a mean squared error sense are those for which the given transform provides a close approximation using sparse nonlinear approximants we show the nature of the constructed estimators and how these estimators relate to the utilized transform and its sparsity over regions of interest the developed estimation framework is general and can readily be applied to other nonstationary signals with a suitable choice of linear transforms part i discusses fundamental issues and part ii is devoted to adaptive algorithms with extensive simulation examples that demonstrate the power of the proposed techniques

cluster validity measure and merging system for hierarchical clustering considering outliers clustering algorithms have evolved to handle more and more complex structures however the measures that allow to qualify the quality of such clustering partitions are rare and have been developed only for specific algorithms in this work we propose a new cluster validity measure cvm to quantify the clustering performance of hierarchical algorithms that handle overlapping clusters of any shape and in the presence of outliers this work also introduces a cluster merging system cms to group clusters that share outliers when located in regions of cluster overlap these outliers may be issued by a mixture of nearby cores the proposed cvm and cms are applied to hierarchical extensions of the support vector and gaussian process clustering algorithms both in synthetic and real experiments these results show that the proposed metrics help to select the appropriate level of hierarchy and the appropriate hyperparameters highlightscluster validity measure for arbitrary shaped clusters with outliers cluster merging system grouping cluster cores based on the outliers structure truly hierarchical variants of support vector and gaussian process clustering benefits for unsupervised change detection applications are presented

dct regularized extreme visual recovery here we study the extreme visual recovery problem in which over 90 of pixel values in a given image are missing existing low rank based algorithms are only effective for recovering data with at most 90 missing values thus we exploit visual data s smoothness property to help solve this challenging extreme visual recovery problem based on the discrete cosine transform dct we propose a novel dct regularizer that involves all pixels and produces smooth estimations in any view our theoretical analysis shows that the total variation regularizer which only achieves local smoothness is a special case of the proposed dct regularizer we also develop a new visual recovery algorithm by minimizing the dct regularizer and nuclear norm to achieve a more visually pleasing estimation experimental results on a benchmark image data set demonstrate that the proposed approach is superior to the state of the art methods in terms of peak signal to noise ratio and structural similarity

knowledge distance measure in multigranulation spaces of fuzzy equivalence relations abstract hierarchical quotient space structure hqss which is a typical representation of multigranulation spaces serves as the essential description tool of fuzzy equivalence relations however current studies on hqss have three main limitations i the inability to reflect the relationship between any two quotient spaces in an hqss ii classification isomorphism cannot characterize the degree of subdivision that exists in an hqss with changing granularities and iii the difficulty in characterizing the difference between two hqsss that are classification isomorphic in this paper we address these issues from the perspective of knowledge distance first we propose a partition based knowledge distance based on the earth mover s distance emd and we prove that our knowledge distance is equivalent to the binary granule based knowledge distance bgkd but is more intuitive than the bgkd then by studying the hierarchy of hqss we conclude that the granularity difference between any two quotient spaces in an hqss is equal to the knowledge distance between them in the view of knowledge distance the concepts of classification isomorphism and subdivision isomorphism are defined and discussed finally we define the concept of the knowledge difference sequence pair which not only discriminates whether two fuzzy equivalence relations are isomorphic but also characterizes the difference between any two hqsss with respect to them theoretical analysis shows that our work is valuable for advancing the application of the hierarchy granular computing theory

image processing using smooth ordering of its patches we propose an image processing scheme based on reordering of its patches for a given corrupted image we extract all patches with overlaps refer to these as coordinates in high dimensional space and order them such that they are chained in the shortest possible path essentially solving the traveling salesman problem the obtained ordering applied to the corrupted image implies a permutation of the image pixels to what should be a regular signal this enables us to obtain good recovery of the clean image by applying relatively simple one dimensional smoothing operations such as filtering or interpolation to the reordered set of pixels we explore the use of the proposed approach to image denoising and inpainting and show promising results in both cases

nonparametric bayesian dictionary learning for analysis of noisy and incomplete images nonparametric bayesian methods are considered for recovery of imagery based upon compressive incomplete and or noisy measurements a truncated beta bernoulli process is employed to infer an appropriate dictionary for the data under test and also for image recovery in the context of compressive sensing significant improvements in image recovery are manifested using learned dictionaries relative to using standard orthonormal image expansions the compressive measurement projections are also optimized for the learned dictionary additionally we consider simpler incomplete measurements defined by measuring a subset of image pixels uniformly selected at random spatial interrelationships within imagery are exploited through use of the dirichlet and probit stick breaking processes several example results are presented with comparisons to other methods in the literature

sparse representation for color image restoration sparse representations of signals have drawn considerable interest in recent years the assumption that natural signals such as images admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data in particular the design of well adapted dictionaries for images has been a major challenge the k svd has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks in this paper we address the problem of learning dictionaries for color images and extend the k svd based grayscale image denoising algorithm that appears in this work puts forward ways for handling nonhomogeneous noise and missing information paving the way to state of the art results in applications such as color image denoising demosaicing and inpainting as demonstrated in this paper

a path optional lossless data hiding scheme based on vq joint neighboring coding data hiding is an important technique for covert communication that embeds secret data into a cover image with minimal perceptible degradation lossless data hiding is a special type of data hiding technique that guarantees not only the secret data but the cover media can be reconstructed without any distortion in this paper a novel path optional lossless data hiding scheme based on the joint neighboring coding jnc of the vector quantization vq index table is proposed the proposed scheme generates a vq index table based on the cover image first next according to an initial key and secret data content different adjacent indices may be chosen to perform joint neighboring coding for each index and hide secret data finally an appropriate output codestream is generated based on the minimal length principle our main contributions lie in three aspects 1 the method combines the novel path based shift method with the traditional jnc method to improve the capacity and stego image quality simultaneously 2 the secret data extraction and cover image recovering processes are separated and both can be publicized to users 3 the method is path optional to meet various users requirements to testify the superiority of the proposed method we compare it with the side match vq smvq based and modified fast correlation vq mfcvq based algorithms according to the experimental results the proposed scheme outperforms the smvq based and mfcvq based algorithms in four aspects i e hiding capacity stego image quality transmission efficiency and security level

color direction patch sparsity based image inpainting using multidirection features this paper proposes a color direction patch sparsity based image inpainting method to better maintain structure coherence texture clarity and neighborhood consistence of the inpainted region of an image the method uses super wavelet transform to estimate the multi direction features of a degraded image and combines with color information to construct the weighted color direction distance wcdd to measure the difference between two patches based on the wcdd the color direction structure sparsity is defined to obtain a more robust filling order and more suitable multiple candidate patches are searched then the target patches are sparsely represented by the multiple candidate patches under neighborhood consistency constraints in both the color and the multi direction spaces experimental results are presented to demonstrate the effectiveness of the proposed approach on tasks such as scratch removal text removal block removal and object removal the effects of super wavelet transforms and direction features are also investigated

adaptive post filtering of jpeg compressed images considering compressed domain lossless data hiding in this paper we propose a new postprocessing algorithm for jpeg compressed images considering a compressed domain lossless data hiding scheme the conventional compressed domain lossless data hiding scheme can embed information bits into the bitstream while maintaining the file size and jpeg compatibility as a new application of the conventional scheme we design an algorithm of embedding filter coefficients in the bitstream such that the embedded filter can be used to enhance the quality of the decoded image to this end a wiener filter is estimated between the original and decoded images and the coefficients are embedded into the jpeg bitstream moreover the size symmetry and precision of the filter and the number of filters are determined under the constraint of embeddable bits the proposed jpeg decoder extracts the filter coefficients from the bitstream and enhances the quality of the decoded image by applying the wiener filter the proposed algorithm can also work with conventional jpeg postprocessing algorithms so that the performance of such conventional algorithms can be further improved

reversible hiding in dct based compressed images this paper presents a lossless and reversible steganography scheme for hiding secret data in each block of quantized discrete cosine transformation dct coefficients in jpeg images in this scheme the two successive zero coefficients of the medium frequency components in each block are used to hide the secret data furthermore the scheme modifies the quantization table to maintain the quality of the stego image experimental results also confirm that the proposed scheme can provide expected acceptable image quality of stego images and successfully achieve reversibility

fields of experts we develop a framework for learning generic expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks the approach provides a practical method for learning high order markov random field mrf models with potential functions that extend over large pixel neighborhoods these clique potentials are modeled using the product of experts framework that uses non linear functions of many linear filter responses in contrast to previous mrf approaches all parameters including the linear filters themselves are learned from training data we demonstrate the capabilities of this field of experts model with two example applications image denoising and image inpainting which are implemented using a simple approximate inference scheme while the model is trained on a generic image database and is not tuned toward a specific application we obtain results that compete with specialized techniques

local manifold distance based on neighborhood graph reordering in this paper we consider the problem of estimating pair wise signals dissimilarities through the comparison of the underlying local manifold structures aiming to confront with issues such as high geometrical complexity and significant overlap that characterize local manifold structures we propose a novel dissimilarity measure which is based on the reordering of a neighborhood graph using a permutation of its nodes in order to quantify the diversity between two manifolds the dissimilarity function utilizes a measure of reordering efficiency computed on each of the corresponding neighborhood graphs using a permutation derived from the solution of an optimization problem on the opposite graph we study the properties of the proposed measure demonstrating its efficiency in a variety of applications using both 1d and 2d signals additionally by exploiting the links to previous findings from the field of random graphs we introduce a generalization of the above measure based on the notion of multi dimensional ordering finally we perform a thorough evaluation on the problem of face recognition under various challenging conditions obtaining state of the art recognition accuracy with a competitive computational load pair wise dissimilarity between signals is addressed as a manifold to manifold distance diversity between two manifolds is estimated using a measure of reordering efficiency of the corresponding neighborhood graphs multi dimensional ordering of nodes proves to be even more discriminative we demonstrate the efficiency of the proposed measure in a variety of 1d and 2d signals state of the art performance is achieved in face recognition under challenging scenarios

clustering based denoising with locally learned dictionaries in this paper we propose k lld a patch based locally adaptive denoising method based on clustering the given noisy image into regions of similar geometric structure in order to effectively perform such clustering we employ as features the local weight functions derived from our earlier work on steering kernel regression these weights are exceedingly informative and robust in conveying reliable local structural information about the image even in the presence of significant amounts of noise next we model each region or cluster which may not be spatially contiguous by ldquolearningrdquo a best basis describing the patches within that cluster using principal components analysis this learned basis or ldquodictionaryrdquo is then employed to optimally estimate the underlying pixel values using a kernel regression framework an iterated version of the proposed algorithm is also presented which leads to further performance enhancements we also introduce a novel mechanism for optimally choosing the local patch size for each cluster using stein s unbiased risk estimator sure we illustrate the overall algorithm s capabilities with several examples these indicate that the proposed method appears to be competitive with some of the most recently published state of the art denoising methods

exemplar based image inpainting using multiscale graph cuts we present a novel formulation of exemplar based inpainting as a global energy optimization problem written in terms of the offset map the proposed energy function combines a data attachment term that ensures the continuity of reconstruction at the boundary of the inpainting domain with a smoothness term that ensures a visually coherent reconstruction inside the hole this formulation is adapted to obtain a global minimum using the graph cuts algorithm to reduce the computational complexity we propose an efficient multiscale graph cuts algorithm to compensate the loss of information at low resolution levels we use a feature representation computed at the original image resolution this permits alleviation of the ambiguity induced by comparing only color information when the image is represented at low resolution levels our experiments show how well the proposed algorithm performs compared with other recent algorithms

the nonsubsampled contourlet transform theory design and applications in this paper we develop the nonsubsampled contourlet transform nsct and study its applications the construction proposed in this paper is based on a nonsubsampled pyramid structure and nonsubsampled directional filter banks the result is a flexible multiscale multidirection and shift invariant image decomposition that can be efficiently implemented via the a trous algorithm at the core of the proposed scheme is the nonseparable two channel nonsubsampled filter bank nsfb we exploit the less stringent design condition of the nsfb to design filters that lead to a nsct with better frequency selectivity and regularity when compared to the contourlet transform we propose a design framework based on the mapping approach that allows for a fast implementation based on a lifting or ladder structure and only uses one dimensional filtering in some cases in addition our design ensures that the corresponding frame elements are regular symmetric and the frame is close to a tight one we assess the performance of the nsct in image denoising and enhancement applications in both applications the nsct compares favorably to other existing methods in the literature

a novel hierarchical clustering combination scheme based on fuzzy similarity relations clustering combination methods have received considerable attentions in recent years and many ensemble based clustering methods have been introduced however clustering combination techniques have been limited to flat clustering combination and the combination of hierarchical clusterings has yet to be addressed in this paper we address and formalize the concept of hierarchical clustering combination and introduce an algorithmic framework in which multiple hierarchical clusterings could be easily combined in this framework the similarity based description matrices of input hierarchical clusterings are aggregated into a transitive consensus matrix in which the final hierarchy could be formed empirical evaluation by using popular available datasets confirms the superiority of combined hierarchical clustering introduced by our method over the standard single hierarchical clustering methods

laplacian eigenmaps for dimensionality reduction and data representation one of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data we consider the problem of constructing a representation for data lying on a low dimensional manifold embedded in a high dimensional space drawing on the correspondence between the graph laplacian the laplace beltrami operator on the manifold and the connections to the heat equation we propose a geometrically motivated algorithm for representing the high dimensional data the algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering some potential applications and illustrative examples are discussed

image denoising by sparse 3 d transform domain collaborative filtering we propose a novel image denoising strategy based on an enhanced sparse representation in transform domain the enhancement of the sparsity is achieved by grouping similar 2d image fragments e g blocks into 3d data arrays which we call groups collaborative altering is a special procedure developed to deal with these 3d groups we realize it using the three successive steps 3d transformation of a group shrinkage of the transform spectrum and inverse 3d transformation the result is a 3d estimate that consists of the jointly filtered grouped image blocks by attenuating the noise the collaborative filtering reveals even the finest details shared by grouped blocks and at the same time it preserves the essential unique features of each individual block the filtered blocks are then returned to their original positions because these blocks are overlapping for each pixel we obtain many different estimates which need to be combined aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy a significant improvement is obtained by a specially developed collaborative wiener filtering an algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail an extension to color image denoising is also developed the experimental results demonstrate that this computationally scalable algorithm achieves state of the art denoising performance in terms of both peak signal to noise ratio and subjective visual quality

a comprehensive framework for image inpainting inpainting is the art of modifying an image in a form that is not detectable by an ordinary observer there are numerous and very different approaches to tackle the inpainting problem though as explained in this paper the most successful algorithms are based upon one or two of the following three basic techniques copy and paste texture synthesis geometric partial differential equations pdes and coherence among neighboring pixels we combine these three building blocks in a variational model and provide a working algorithm for image inpainting trying to approximate the minimum of the proposed energy functional our experiments show that the combination of all three terms of the proposed energy works better than taking each term separately and the results obtained are within the state of the art

watermarking and authentication of quantum images based on restricted geometric transformations a secure keyless and blind watermarking and authentication strategy for images on quantum computers waqi is proposed based on restricted geometric transformations in contrast with conventional digital watermarking techniques where geometric transformations on the contents of an image are considered undesirable the proposed waqi scheme utilises the restricted variants of the quantum versions of these transformations as the main resources of the watermark embedding and authentication circuits this is accomplished by a careful analysis of the classical content of the image watermark pair based on which a bespoke watermark map that translates into the gate sequences of the quantum watermark embedding and authentication circuits is realised simulation based experimental results involving the classical i e conventional or non quantum simulation of the input images watermark signals and quantum circuits yielded a 25 improvement in terms of overall watermark embedding capacity and between 7 and 50 7 in terms of the visible quality of the watermarked images in comparison with select digital watermarking methods for various pairs thus demonstrating both the feasibility and capabilities of the proposed waqi scheme when the necessary quantum hardware are realised physically this scheme provides the framework for representing two or more quantum data as a single quantum circuit and opens the door for other applications aimed at quantum data protection

a lossless data hiding scheme based on three pixel block differences this paper proposes a data hiding scheme that losslessly embeds a message into a cover image using the two differences between the first and the second pixel as well as between the second and the third pixel in a three pixel block in the cover image an absolute difference between a pair of pixels is selected to embed the message if the number of pixel pairs with the difference in the image is the largest to embed a bit 1 or 0 the selected difference is increased by 1 or left unchanged respectively in the best case a three pixel block can embed two bits 11 and only the central pixel needs to be increased or decreased by 1 the average payload capacity among the test images can be up to 2 08 bits per pixel bpp

a unified data embedding and scrambling method conventionally data embedding techniques aim at maintaining high output image quality so that the difference between the original and the embedded images is imperceptible to the naked eye recently as a new trend some researchers exploited reversible data embedding techniques to deliberately degrade image quality to a desirable level of distortion in this paper a unified data embedding scrambling technique called ues is proposed to achieve two objectives simultaneously namely high payload and adaptive scalable quality degradation first a pixel intensity value prediction method called checkerboard based prediction is proposed to accurately predict 75 of the pixels in the image based on the information obtained from 25 of the image then the locations of the predicted pixels are vacated to embed information while degrading the image quality given a desirable quality quantified in ssim for the output image ues guides the embedding scrambling algorithm to handle the exact number of pixels i e the perceptual quality of the embedded scrambled image can be controlled in addition the prediction errors are stored at a predetermined precision using the structure side information to perfectly reconstruct or approximate the original image in particular given a desirable ssim value the precision of the stored prediction errors can be adjusted to control the perceptual quality of the reconstructed image experimental results confirmed that ues is able to perfectly reconstruct or approximate the original image with ssim value after completely degrading its perceptual quality while embedding at 7 001 bpp on average

reversible data hiding exploiting spatial correlation between sub sampled images reversible data hiding enables host media to be restored from marked media without any loss of host information since this reversibility helps to make right decision during image analysis it is highly desired in quality sensitive imagery where even the minimal distortion introduced by embedding data is unacceptable in this paper we propose a reversible data hiding method that modifies the difference histogram between sub sampled images it exploits the high spatial correlation inherent in neighboring pixels to achieve high capacity and imperceptible embedding on various test images including 16 bit images we demonstrate the validity of our proposed method by comparing to other existing reversible data hiding algorithms experimental results support that our method provides high embedding capacity while keeping the distortions at a low level

data hiding for vector quantization images using mixed base notation and dissimilar patterns without loss of fidelity data hiding without loss of fidelity also called reversible data hiding allows an original image to be restored completely after extraction of hidden data embedded in a cover image in this paper we propose a reversible scheme based on mixed base notation and dissimilar pattern strategies for vector quantization vq compressed images the dissimilar patterns resulting from the declustering process make the proposed hiding method more efficient our experimental results show that the time required for the embedding process in the proposed method is much less than that in the side matched vq smvq based reversible method in addition the use of declustering can reduce many more prediction errors than traditional clustering methods the results also show that the number of declustered groups and the spatial characteristics of the cover image will affect the embedding capacity and the size of the stego vq index table

adaptive subspace based inverse projections via division into multiple sub problems for missing image data restoration this paper presents adaptive subspace based inverse projections via division into multiple sub problems asip dimss for missing image data restoration in the proposed method a target problem for estimating missing image data is divided into multiple sub problems and each sub problem is iteratively solved with the constraints of other known image data by projection into a subspace model of image patches the solution of each sub problem is calculated where we call this procedure subspace based inverse projection for simplicity the proposed method can use higher dimensional subspaces for finding unique solutions in each sub problem and successful restoration becomes feasible since a high level of image representation performance can be preserved this is the biggest contribution of this paper furthermore the proposed method generates several subspaces from known training examples and enables derivation of a new criterion in the above framework to adaptively select the optimal subspace for each target patch in this way the proposed method realizes missing image data restoration using asip dims since our method can estimate any kind of missing image data its potential in two image restoration tasks image inpainting and super resolution based on several methods for multivariate analysis is also shown in this paper

efficient reversible data hiding for color filter array images a reversible data hiding algorithm which uses prediction errors in the color difference domain for mosaic images with the bayer color filter array cfa is proposed furthermore the proposed algorithm can be extended to deal with the digital time delay and integration dtdi mosaic images and lukac and plataniotis lp mosaic images experimental results on cfa dtdi and lp mosaic images demonstrate that the proposed algorithm can achieve high embedding capacity while maintaining good image quality

reversible steganographic method using smvq approach based on declustering reversible steganography allows an original image to be completely restored after the extraction of hidden data embedded in a cover image in this paper we propose a reversible scheme for vq compressed images that is based on a declustering strategy and takes advantage of the local spatial characteristics of the image the main advantages of our method are ease of implementation low computational demands and no requirement for auxiliary data the experimental results show that the embedding capacity of the proposed method is controlled by the number of declustered groups and the spatial characteristics of the cover image the results also show that the proposed method has the best embedding capacity and the least running time compared with other methods

mathematical models for local nontexture inpaintings dedicated to stanley osher on the occasion of his 60th birthday abstract inspired by the recent work of bertalmio et al on digital inpaintings siggraph 2000 we develop general mathematical models for local inpaintings of nontexture images on smooth regions inpaintings are connected to the harmonic and biharmonic extensions and inpainting orders are analyzed for inpaintings involving the recovery of edges we study a variational model that is closely connected to the classical total variation tv denoising model of rudin osher and fatemi phys d 60 1992 pp 259 268 other models are also discussed based on the mumford shah regularity comm pure appl math xlii 1989 pp 577 685 and curvature driven diffusions cdd of chan and shen j visual comm image rep 12 2001 the broad applications of the inpainting models are demonstrated through restoring scratched old photos disocclusion in vision analysis text removal digital zooming and edge based image coding