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Showing papers on "Contourlet published in 2010"


Journal ArticleDOI
Bin Yang1, Shutao Li1
TL;DR: A sparse representation-based multifocus image fusion method that can simultaneously resolve the image restoration and fusion problem by changing the approximate criterion in the sparse representation algorithm is proposed.
Abstract: To obtain an image with every object in focus, we always need to fuse images taken from the same view point with different focal settings. Multiresolution transforms, such as pyramid decomposition and wavelet, are usually used to solve this problem. In this paper, a sparse representation-based multifocus image fusion method is proposed. In the method, first, the source image is represented with sparse coefficients using an overcomplete dictionary. Second, the coefficients are combined with the choose-max fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. Furthermore, the proposed fusion scheme can simultaneously resolve the image restoration and fusion problem by changing the approximate criterion in the sparse representation algorithm. The proposed method is compared with spatial gradient (SG)-, morphological wavelet transform (MWT)-, discrete wavelet transform (DWT)-, stationary wavelet transform (SWT)-, curvelet transform (CVT)-, and nonsubsampling contourlet transform (NSCT)-based methods on several pairs of multifocus images. The experimental results demonstrate that the proposed approach performs better in both subjective and objective qualities.

571 citations


Journal ArticleDOI
TL;DR: A review on the curvelet transform, including its history beginning from wavelets, its logical relationship to other multiresolution multidirectional methods like contourlets and shearlets, and its basic theory and discrete algorithm is presented.
Abstract: Multiresolution methods are deeply related to image processing, biological and computer vision, and scientific computing. The curvelet transform is a multiscale directional transform that allows an almost optimal nonadaptive sparse representation of objects with edges. It has generated increasing interest in the community of applied mathematics and signal processing over the years. In this article, we present a review on the curvelet transform, including its history beginning from wavelets, its logical relationship to other multiresolution multidirectional methods like contourlets and shearlets, its basic theory and discrete algorithm. Further, we consider recent applications in image/video processing, seismic exploration, fluid mechanics, simulation of partial different equations, and compressed sensing.

410 citations


Proceedings ArticleDOI
24 Mar 2010
TL;DR: Block-based random image sampling is coupled with a projection-driven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image, yielding images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation.
Abstract: Recent years have seen significant interest in the paradigm of compressed sensing (CS) which permits, under certain conditions, signals to be sampled at sub-Nyquist rates via linear projection onto a random basis while still enabling exact reconstruction of the original signal. As applied to 2D images, however, CS faces several challenges including a computationally expensive reconstruction process and huge memory required to store the random sampling operator. Recently, several fast algorithms have been developed for CS reconstruction, while the latter challenge was addressed by Gan using a block-based sampling operation as well as projection-based Landweber iterations to accomplish fast CS reconstruction while simultaneously imposing smoothing with the goal of improving the reconstructed-image quality by eliminating blocking artifacts. In this technique, smoothing is achieved by interleaving Wiener filtering with the Landweber iterations, a process facilitated by the relative simple implementation of the Landweber algorithm. In this work, we adopt Gan's basic framework of block-based CS sampling of images coupled with iterative projection-based reconstruction with smoothing. Our contribution lies in that we cast the reconstruction in the domain of recent transforms that feature a highly directional decomposition. These transforms---specifically, contourlets and complex-valued dual-tree wavelets---have shown promise to overcome deficiencies of widely-used wavelet transforms in several application areas. In their application to iterative projection-based CS recovery, we adapt bivariate shrinkage to their directional decomposition structure to provide sparsity-enforcing thresholding, while a Wiener-filter step encourages smoothness of the result. In experimental simulations, we find that the proposed CS reconstruction based on directional transforms outperforms equivalent reconstruction using common wavelet and cosine transforms. Additionally, the proposed technique usually matches or exceeds the quality of total-variation (TV) reconstruction, a popular approach to CS recovery for images whose gradient-based operation also promotes smoothing but runs several orders of magnitude slower than our proposed algorithm.

387 citations


Journal ArticleDOI
Shuyuan Yang1, Min Wang1, Licheng Jiao1, Ruixia Wu1, Zhaoxia Wang1 
TL;DR: A new contourlet packet is constructed based on a complete wavelet quadtree followed by a nonsubsampled directional filter bank, which has more accurate reconstruction of images than WP and shows the superiorities of the method to its counterparts in image clarity and some numerical guidelines.

190 citations


Journal ArticleDOI
Xiaobo Qu1, Weiru Zhang1, Di Guo1, Congbo Cai1, Shuhui Cai1, Zhong Chen1 
TL;DR: Simulation results demonstrate that contourlet-based CS-MRI can better reconstruct the curves and edges than traditional wavelet- based methods, especially at low k-space sampling rate.
Abstract: Reducing the acquisition time is important for clinical magnetic resonance imaging (MRI). Compressed sensing has recently emerged as a theoretical foundation for the reconstruction of magnetic resonance images from undersampled k-space measurements, assuming those images are sparse in a certain transform domain. However, most real-world signals are compressible rather than exactly sparse. For example, the commonly used two-dimensional wavelet for compressed sensing MRI (CS-MRI) does not sparsely represent curves and edges. In this article, we introduce a geometric image transform, the contourlet, to overcome this shortage. In addition, the improved redundancy provided by the contourlet can successfully suppress the pseudo-Gibbs phenomenon, a tiresome artefact produced by undersampling of k-space, around the singularities of images. For numerical calculation, a simple but effective iterative thresholding algorithm is employed to solve l 1 norm optimization for CS-MRI. Considering the recovered information ...

156 citations


Journal ArticleDOI
TL;DR: Experimental results confirm the superiority of the proposed method against common attacks, such as Additive White Gaussian Noise (AWGN), JPEG compression, and rotation attacks, in comparison with the recently proposed techniques.
Abstract: In this paper, an improved multiplicative image watermarking system is presented. Since human visual system is less sensitive to the image edges, watermarking is applied in the contourlet domain, which represents image edges sparsely. In the presented scheme, watermark data is embedded in directional subband with the highest energy. By modeling the contourlet coefficients with General Gaussian Distribution (GGD), the distribution of watermarked noisy coefficients is analytically calculated. The tradeoff between the transparency and robustness of the watermark data is solved in a novel fashion. At the receiver, based on the Maximum Likelihood (ML) decision rule, an optimal 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. Experimental results confirm the superiority of the proposed method against common attacks, such as Additive White Gaussian Noise (AWGN), JPEG compression, and rotation attacks, in comparison with the recently proposed techniques.

146 citations


Journal ArticleDOI
TL;DR: Experimental results illustrate that the contourlet-based feature extraction in conjunction with the state-of-art classifiers construct a powerful, efficient and practical approach for automatic mass classification of mammograms.

118 citations


Journal ArticleDOI
Shutao Li1, Bin Yang1
TL;DR: This work proposes a hybrid multiresolution method by combining the SWT with the NSCT to perform image fusion, and demonstrates that the SSAN method performs better than SNAS and the individualMultiresolution-based methods, such as NSCT, SWT, complex wavelet (CWT), curvelet (CVT) and wavelet-based contourlet (WBCT).
Abstract: The aim of image fusion is to integrate complementary information from several images to create a highly informative image which is more suitable for human visual perception or computer-processing tasks. Recent studies show that stationary wavelet transform (SWT) and nonsubsampled contourlet transform (NSCT) both turn out to be effective and efficient for image fusion. In order to take some complementary characteristics between the two multiresolution transformations simultaneously, we propose a hybrid multiresolution method by combining the SWT with the NSCT to perform image fusion. Two methods, serial NSCT aiding SWT (SNAS) and serial SWT aiding NSCT (SSAN), are studied and compared with some state-of-the-art methods. Experimental results demonstrate that the SSAN method performs better than SNAS and the individual multiresolution-based methods, such as NSCT, SWT, complex wavelet (CWT), curvelet (CVT) and wavelet-based contourlet (WBCT).

105 citations


Journal ArticleDOI
TL;DR: The contourlet transform is utilized to NSS of images, and then the relationship of contour let coefficients is represented by the joint distribution, and an image-dependent threshold is adopted to reduce the effect of content to the statistical model.

80 citations


Journal ArticleDOI
TL;DR: This paper proposes to utilize the logarithmic nonsubsampled contourlet transform (LNSCT) to estimate the reflectance component from a single face image and refer it as the illumination invariant feature for face recognition, where NSCT is a fully shift-invariant, multi-scale, and multi-direction transform.

74 citations


Journal ArticleDOI
TL;DR: Two rotation invariant watermark embedding schemes in the non-subsampled contourlet transform (NSCT) domain based on the scale-adapted local regions are presented and can efficiently resist both signal processing attacks and geometric attacks.

Journal ArticleDOI
TL;DR: The experimental results showed that the combination of Bayesian inference and bandelet transform outperforms the contourlet-based despeckling algorithm using synthetic data and objective measurements.
Abstract: This letter presents the despeckling of synthetic aperture radar (SAR) images within the bandelet and contourlet domains. A model-based approach is presented for the despeckling of SAR images. The speckle-reduced estimate is found using the first-order Bayesian inference, and the best model's parameters are estimated using the second-order Bayesian inference. Synthetic and real images are used for evaluating the qualities of the despeckling methods. The experimental results showed that the combination of Bayesian inference and bandelet transform outperforms the contourlet-based despeckling algorithm using synthetic data and objective measurements.

Journal ArticleDOI
TL;DR: Experimental results on the Yale B, the extended Yale and the CMU PIE face databases show that the proposed algorithm can effectively alleviate the effect of illumination on face recognition.

Journal ArticleDOI
TL;DR: It is demonstrated, through the experiments, that choosing suitable cover image by a proper selection measure could help the steganographer reduce detectability of stego images and the effect of cover selection on steganography embedding and steganalysis results is investigated.
Abstract: In this paper, we present a new adaptive contourlet-based steganography method that hides secret data in a specific or automatically selected cover image. Our proposed steganography method primarily decomposes the cover image by contourlet transform. Then, every bit of secret data is embedded by increasing or decreasing the value of one coefficient in a block of a contourlet subband. Contourlet coefficients are manipulated relative to their magnitudes to hide the secret data adaptively. In addition to proposing contourlet-based steganography method, this work investigates the effect of cover selection on steganography embedding and steganalysis results. We demonstrate, through the experiments, that choosing suitable cover image by a proper selection measure could help the steganographer reduce detectability of stego images. The proposed technique is examined with some state-of-the-art steganalysis methods, and the results illustrate that an image can successfully hide secret data with average embedding capacity of 0.02 bits per pixel in a random selected cover image. Cover selection improves the embedding capacity up to 0.06 bits per pixel. Several experiments and comparative studies are performed to show the effectiveness of the proposed technique in enhancing the security of stego images, as well as to demonstrate its gain over the previous approaches in literature.

Journal ArticleDOI
TL;DR: Among these transforms the DCWT is preferred both in terms of performance and computational cost; the best window size for denoising depends on the noise level and type of image; and incorporating interscale dependency into the Denoising process results in some improvement only for uncrowded images.
Abstract: In this study, the authors fit three univariate mixture distributions to the image coefficients in four sparse domains [ordinary discrete wavelet transform (DWT), discrete complex wavelet transform (DCWT), discrete contourlet transform (DCOT) and discrete curvelet transform (DCUT)]. By estimating the parameters of these mixture priors locally using adjacent coefficients in the same scale, the authors characterise the heavy-tailed nature and the intrascale statistical dependency of these coefficients. Using these mixture-local-priors, the authors derive estimators using maximum a posteriori (MAP) and minimum mean squared error (MMSE) for image denoising. Using the proposed shrinkage functions in these sparse domains for various window sizes from our simulations, we conclude that: (i) among these transforms the DCWT is preferred both in terms of performance and computational cost; (ii) the best window size for denoising depends on the noise level and type of image; (iii) incorporating interscale dependency into the denoising process results in some improvement only for uncrowded images, and (iv) the MMSE estimators outperform the MAP estimators if the input peak signal-to-noise ratio (PSNR) is greater than 28 dB and the MAP estimators are preferred for PSNR smaller than 22 dB.

Proceedings ArticleDOI
19 Nov 2010
TL;DR: From the experimental results, it is observed that RDWT method provides better information (quality) using EN metric and the Contour let Transform gives the difference in source to the fusion image using OCE metric and also the fused image obtained from the proposed fusion techniques has more information than the source images are proved through all metrics.
Abstract: Image fusion is the process of combining relevant information from two or more images into a single fused image. The resulting image will be more informative than any of the input images. The fusion in medical images is necessary for efficient diseases diagnosis from multimodality, multidimensional and multiparameter type of images. This paper describes a multimodality medical image fusion system using different fusion techniques and the resultant is analysed with quantitative measures. Initially, the registered images from two different modalities such as CT (anatomical information) and MRI - T2, FLAIR (pathological information) are considered as input, since the diagnosis requires anatomical and pathological information. Then the fusion techniques namely Redundancy Discrete Wavelet Transform (RDWT) and Contour let Transform are applied. Further the fused image is analyzed with five types of quantitative metrics such as Standard Deviation (SD), Entropy (EN), Overall Cross Entropy (OCE), Ratio of Spatial Frequency Error (RSFE), and Power Signal to Noise Ratio (PSNR) for performance evaluation. From the experimental results we observed that RDWT method provides better information (quality) using EN metric and the Contour let Transform gives the difference in source to the fused image using OCE metric and also the fused image obtained from the proposed fusion techniques has more information than the source images are proved through all metrics.

Journal ArticleDOI
Sung Min Gho1, Yoonho Nam1, Sang Young Zho1, Eung Yeop Kim1, Donghyun Kim1 
TL;DR: The results show that the proposed CS algorithm achieves a more accurate reconstruction in terms of the mean structural similarity index and root mean square error than the CS algorithm using wavelet transform.

Journal ArticleDOI
Qi Zhang1, Yuanyuan Wang1, Weiqi Wang1, Jianying Ma1, Juying Qian1, Junbo Ge1 
TL;DR: The proposed image segmentation method based on snakes and the Contourlet transform can automatically and accurately detect calcifications and delineate their boundaries, and it outperformed a recently proposed method, the Santos Filho method, in terms of the sensitivity and specificity of calcification detection.
Abstract: It is valuable to detect calcifications in intravascular ultrasound images for studies of coronary artery diseases. An image segmentation method based on snakes and the Contourlet transform is proposed to automatically and accurately detect calcifications. With the Contourlet transform, an original image is decomposed into low-pass bands and band-pass directional sub-bands. The 2-D Renyi's entropy is used to adaptively threshold the low-pass bands in a multiresolution hierarchy to determine regions-of-interest (ROIs). Then a mean intensity ratio, reflecting acoustic shadowing, is presented to classify calcifications from noncalcifications and obtain initial contours of calcifications. The anisotropic diffusion is used in bandpass directional sub-bands to suppress noise and preserve calcific edges. Finally, the contour deformation in the boundary vector field is used to obtain final contours of calcifications. The method was evaluated via 60 simulated images and 86 in vivo images. It outperformed a recently proposed method, the Santos Filho method, by 2.76% and 14.53%, in terms of the sensitivity and specificity of calcification detection, respectively. The area under the receiver operating characteristic curve increased by 0.041. The relative mean distance error, relative difference degree, relative arc difference, relative thickness difference and relative length difference were reduced by 5.73%, 19.79%, 11.62%, 12.06% and 20.51%, respectively. These results reveal that the proposed method can automatically and accurately detect calcifications and delineate their boundaries. (E-mail: yywang@fudan.edu.cn).

Proceedings ArticleDOI
07 Oct 2010
TL;DR: In this paper, a novel infrared and visible image fusion method based on nonsubsampled contourlet transform (NSCT) and fuzzy logic is proposed, where the degree of membership to the background and the target for each pixel in the low frequency subband of the IR image is determined by using fuzzy logic.
Abstract: A novel infrared (IR) and visible image fusion method based on nonsubsampled contourlet transform (NSCT) and fuzzy logic is proposed. Input IR and visible images are decomposed into a series of low frequency and high frequency subbands by using NSCT. The degree of membership to the background and the target for each pixel in the low frequency subband of the IR image is determined by using fuzzy logic. An adaptive weighted average is then taken as the fusion of low frequency subband coefficients while maximum absolution selection is performed for the fusion of high frequency subband coefficients. The fused image is obtained by taking inverse NSCT of the fused coefficients. Experimental results with real IR and visible images show that the proposed method effectively enhances infrared targets and preserves details of the visible image.

Journal ArticleDOI
TL;DR: Experimental results show that the contourlet features are very stable features for invariant palmprint classification and handwritten numeral recognition, and better classification rates are reported when compared with other existing classification methods.

Journal ArticleDOI
Xin Zhang1, Xili Jing1
TL;DR: Experimental results prove that the new method can remove Gaussian white noise effectively, reserve image edges better and enhance the peak signal-to-noise ratio.

Journal ArticleDOI
TL;DR: The proposed nonblind multiresolution watermarking method can embed considerable payload, while providing good perceptual transparency and resistance to many attacks, it is a suitable algorithm for fingerprinting applications.
Abstract: We propose a new nonblind multiresolution watermarking method for still images based on the contourlet transform (CT). In our approach, the watermark is a grayscale image which is embedded into the highest frequency subband of the host image in its contourlet domain. We demonstrate that in comparison to other methods, this method enables us to embed more amounts of data into the directional subbands of the host image without degrading its perceptibility. The experimental results show robustness against several common watermarking attacks such as compression, adding noise, filtering, and geometrical transformations. Since the proposed approach can embed considerable payload, while providing good perceptual transparency and resistance to many attacks, it is a suitable algorithm for fingerprinting applications.

Proceedings ArticleDOI
Xiaobo Qu1, Xue Cao1, Di Guo1, Changwei Hu1, Zhong Chen1 
14 Mar 2010
TL;DR: Simulation results demonstrate that the proposed method can improve image quality when comparing to single sparsifying transform, and is implemented via the state-of-art smoothed l0 norm in overcomplete sparse decomposition.
Abstract: Undersampling the k-space is an efficient way to speed up the magnetic resonance imaging (MRI). Recently emerged compressed sensing MRI shows promising results. However, most of them only enforce the sparsity of images in single transform, e.g. total variation, wavelet, etc. In this paper, based on the principle of basis pursuit, we propose a new framework to combine sparsifying transforms in compressed sensing MRI. Each transform can efficiently represent specific feature that the other can not. This framework is implemented via the state-of-art smoothed l 0 norm in overcomplete sparse decomposition. Simulation results demonstrate that the proposed method can improve image quality when comparing to single sparsifying transform.

Proceedings ArticleDOI
14 Mar 2010
TL;DR: The reconstruction problem of compressive sensing algorithm that is exploited for image compression, is investigated, and by adding some new constraints compatible with typical image properties, the performance of the reconstruction is improved.
Abstract: In this paper, the reconstruction problem of compressive sensing algorithm that is exploited for image compression, is investigated. Considering the Total Variation (TV) minimization algorithm, and by adding some new constraints compatible with typical image properties, the performance of the reconstruction is improved. Using DCT and contourlet transforms, sparse expansion of the image are exploited to provide new constraints to remove irrelevant vectors from the feasible set of the optimization problem while keeping the problem as a standard Second Order Cone Programming (SOCP) one. Experimental results show that, the proposed method, with new constraints, outperforms the conventional TV minimization method by up to 2 dB in PSNR.

01 Jan 2010
TL;DR: Results provide evidence that CT based texture features can be successfully applied for the classification of different types of texture in ultrasound thyroid images and show that the proposed methodology is more efficient than previous thyroid ultrasound representation methods proposed in the literature.
Abstract: Ultrasonography is an invaluable and widely used medical imaging tool. Nevertheless, automatic texture analysis on ultrasound images remains a challenging issue. This work presents and investigates a texture representation scheme on thyroid ultrasound images for the detection of hypoechoic and isoechoic thyroid nodules, which present the highest malignancy risk. The proposed scheme is based on the Contourlet Transform (CT) and incorporates a thresholding approach for the selection of the most significant CT coefficients. Then a variety of statistical texture features are evaluated and the optimal subsets are extracted through a selection process. A Gaussian kernel Support Vector Machine (SVM) classifier is applied along the Sequential Floating Forward Selection (SFFS) algorithm, in order to investigate the most representative set of CT features. For this experimental evaluation, two image datasets have been utilized: one consisting of hypoechoic nodules and normal thyroid tissue and another of isoechoic nodules and normal thyroid tissue. Comparative experiments show that the proposed methodology is more efficient than previous thyroid ultrasound representation methods proposed in the literature. The maximum classification accuracy reached 95% for hypoechoic dataset, and 92% for isoechoic dataset. Such results provide evidence that CT based texture features can be successfully applied for the classification of different types of texture in ultrasound thyroid images.

Posted Content
TL;DR: This paper gives an overview of statistical methodologies and techniques employed for texture feature extraction using most popular spatial-frequency image transforms, namely discrete wavelets, Gabor wavelet, dual-tree complex wavelet and contourlets.
Abstract: The advent of large scale multimedia databases has led to great challenges in content-based image retrieval (CBIR). Even though CBIR is considered an emerging field of research, however it constitutes a strong background for new methodologies and systems implementations. Therefore, many research contributions are focusing on techniques enabling higher image retrieval accuracy while preserving low level of computational complexity. Image retrieval based on texture features is receiving special attention because of the omnipresence of this visual feature in most real-world images. This paper highlights the state-of-the-art and current progress relevant to texture-based image retrieval and spatial-frequency image representations. In particular, it gives an overview of statistical methodologies and techniques employed for texture feature extraction using most popular spatial-frequency image transforms, namely discrete wavelets, Gabor wavelets, dual-tree complex wavelet and contourlets. Indications are also given about used similarity measurement functions and most important achieved results.

Patent
02 Jun 2010
TL;DR: In this article, a blending method of infrared and multi-colored visible light images based on the information of multicolumn transmission and entropy has been proposed, which can effectively extract the abundant background information in the visible light image and the target information in infrared image.
Abstract: The invention discloses a blending method of infrared and multi-colored visible light images based on the information of multi-colored transmission and entropy. The process of the method is as follows: three channel images of R, G, and B of the multi-colored visible light images are calculated to obtain a typical value, thus obtaining visible light images with gray scale; the visible light imageswith gray scale and infrared images are decomposed by adopting non sampling Contourlet conversion; low frequency sub-band coefficient blending rules are constructed based on the infrared images and visible light physical characteristics, bandpass direction sub-band coefficient blending rules are constructed based on the combination of the entropy of local region direction information and region energy, the coefficient of transformation of source images are combined, and the coefficient of transformation combined carries out the non sampling Contourlet conversion to obtain blending image with gray scale; the multi-colored information of the visible light images is transmitted to the blending images by adopting a multi-colored transmission method based on 1 alpha beta color space, thus obtaining the multi-colored blending images. The blending method not only can effectively extract the abundant background information in the visible light images and the target information in the infraredimages, but also can keep nature multi-colored information in the visible light images.

Journal ArticleDOI
14 Sep 2010-Sensors
TL;DR: An efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT) that has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise.
Abstract: Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT). An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach, while the second technique has led to the best performance results of all. Moreover, to demonstrate the effectiveness of these methods, these registration techniques have been successfully applied to register SPOT, IKONOS and Synthetic Aperture Radar (SAR) images. The algorithm has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise.

Proceedings ArticleDOI
04 Nov 2010
TL;DR: Experimental results show that this novel image forensics method can detect possible blurring in images and locate the tampering boundary with a relative high accurate rate.
Abstract: In this paper, a novel image forensics method is proposed to detect manual blurred edges from a tampered image. Firstly, the image edges are analyzed by using non-subsampled contourlet transform. Then the differences between the normal edge and the blurred edge are extracted by researching phase congruency and prediction-error image. After that, the features are used to train the SVM, by which the blurred edges can be distinguished. Finally, the local definition is introduced to indicate the differences between the manual blur and defocus ones. Experimental results show that this method can detect possible blurring in images and locate the tampering boundary with a relative high accurate rate.

Journal ArticleDOI
01 Dec 2010
TL;DR: An universal approach to steganalysis called CBS, which uses statistical moments of contourlet coefficients as features for analysis and a non-linear SVM classifier is applied to classify cover and stego images.
Abstract: An ideal steganographic technique embeds secret information into a carrier cover object with virtually imperceptible modification of the cover object. Steganalysis is a technique to discover the presence of hidden embedded information in a given object. Each steganalysis method is composed of feature extraction and feature classification components. Using features that are more sensitive to information hiding yields higher success in steganalysis. So far, several steganalysis methods have been presented which extract some features from DCT or wavelet coefficients of images. Multi-scale and time-frequency localization of an image is offered by wavelets. However, wavelets are not effective in representing the images in different directions. Contourlet transform addresses this problem by providing two additional properties, directionality and anisotropy. The present paper offers an universal approach to steganalysis called CBS, which uses statistical moments of contourlet coefficients as features for analysis. After feature extraction, a non-linear SVM classifier is applied to classify cover and stego images. The efficiency of the proposed method is demonstrated by experimental investigations. The proposed steganalysis method is compared with two well-known steganalyzers against typical steganography methods. The results showed the superior performance of our method.