Showing papers on "Contourlet published in 2009"

Block compressed sensing of images using directional transforms

Abstract: 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. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing.

Image Quality Assessment Based on Multiscale Geometric Analysis

Abstract: Reduced-reference (RR) image quality assessment (IQA) has been recognized as an effective and efficient way to predict the visual quality of distorted images. The current standard is the wavelet-domain natural image statistics model (WNISM), which applies the Kullback-Leibler divergence between the marginal distributions of wavelet coefficients of the reference and distorted images to measure the image distortion. However, WNISM fails to consider the statistical correlations of wavelet coefficients in different subbands and the visual response characteristics of the mammalian cortical simple cells. In addition, wavelet transforms are optimal greedy approximations to extract singularity structures, so they fail to explicitly extract the image geometric information, e.g., lines and curves. Finally, wavelet coefficients are dense for smooth image edge contours. In this paper, to target the aforementioned problems in IQA, we develop a novel framework for IQA to mimic the human visual system (HVS) by incorporating the merits from multiscale geometric analysis (MGA), contrast sensitivity function (CSF), and the Weber's law of just noticeable difference (JND). In the proposed framework, MGA is utilized to decompose images and then extract features to mimic the multichannel structure of HVS. Additionally, MGA offers a series of transforms including wavelet, curvelet, bandelet, contourlet, wavelet-based contourlet transform (WBCT), and hybrid wavelets and directional filter banks (HWD), and different transforms capture different types of image geometric information. CSF is applied to weight coefficients obtained by MGA to simulate the appearance of images to observers by taking into account many of the nonlinearities inherent in HVS. JND is finally introduced to produce a noticeable variation in sensory experience. Thorough empirical studies are carried out upon the LIVE database against subjective mean opinion score (MOS) and demonstrate that 1) the proposed framework has good consistency with subjective perception values and the objective assessment results can well reflect the visual quality of images, 2) different transforms in MGA under the new framework perform better than the standard WNISM and some of them even perform better than the standard full-reference IQA model, i.e., the mean structural similarity index, and 3) HWD performs best among all transforms in MGA under the framework.

Reduced-Reference IQA in Contourlet Domain

Abstract: The human visual system (HVS) provides a suitable cue for image quality assessment (IQA). In this paper, we develop a novel reduced reference (RR) IQA scheme by incorporating the merits from the contourlet transform, contrast sensitivity function (CSF), and Weber's law of just noticeable difference (JND). In this scheme, the contourlet transform is utilized to decompose images and then extract features to mimic the multichannel structure of HVS. CSF is applied to weight coefficients obtained by the contourlet transform to simulate the appearance of images to observers by taking into account many of the nonlinearities inherent in HVS. JND is finally introduced to produce a noticeable variation in sensory experience. Thorough empirical studies are carried out upon the laboratory for image and video engineering database against the subjective mean opinion score and demonstrate that the proposed framework has good consistency with subjective perception values and the objective assessment results can well reflect the visual quality of images.

Shearlet-Based Deconvolution

Abstract: In this paper, a new type of deconvolution algorithm is proposed that is based on estimating the image from a shearlet decomposition. Shearlets provide a multidirectional and multiscale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. Constructions such as curvelets and contourlets share similar properties, yet their implementations are significantly different from that of shearlets. Taking advantage of unique properties of a new M-channel implementation of the shearlet transform, we develop an algorithm that allows for the approximation inversion operator to be controlled on a multiscale and multidirectional basis. A key improvement over closely related approaches such as ForWaRD is the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation (GCV). Various tests show that this method can perform significantly better than many competitive deconvolution algorithms.

Offline Handwritten Signature Identification and Verification Using Contourlet Transform

Abstract: In this paper, a new offline handwritten signature identification and verification system based on Contourlet transform is proposed. Contourlet transform (CT) is used as feature extractor in proposed system. Signature image is enhanced by removing noise and then it is normalized by size. After preprocessing stage, by applying a special type of Contourlet transform on signature image, related Contourlet coefficients are computed and feature vector is created. Euclidean distance is used as classifier. One of the most important features of proposed system is its independency from signer’s nationality. Experimental results show that proposed system has so reliable results for both Persian and English signatures.

Multifocus image fusion method of sharp frequency localized Contourlet transform domain based on sum-modified-Laplacian

Abstract: In order to suppress the pseudo-Gibbs phenomena around singularities of fused images and to reduce significant amounts of aliasing components located far away from desired supports when the original Contourlet is employed in the image fusion,a multifocus image fusion method in Sharp Frequency Localized Contourlet Transform(SFLCT) domain based on a sum-modified-Laplacian is proposed.The SFLCT,instead of the original Contourlet,is utilized as the multiscale transform to decompose the original multifocus images into subbands.Then,typical measurements for the multifocus image fusion in a spatial domain are introduced to the Contourlet domain and Sum-modified-Laplacian(SML),and the criterion to distinguish SFLCT coefficients from the clear parts or from blurry parts of images are employed in SFCLT subbands to select the SFLCT transform coefficients.Finally,the inverse SFLCT is used to reconstruct fused images.Moreover,a cycle spinning method is applied to compensate for the lack of translation invariance property and to suppress the pseudo-Gibbs phenomena of fused images.Using the proposed fusion method,experimental results demonstrate that the mutual information has improved by 5.87% and transferred edge information QAB/F has improved by 2.70% as compared with those of the cycle spinning wavelet method,and has improved by 1.77% and 1.29% as compared with those of the cycle spinning Contourlet method.Meanwhile,the proposed fusion method has advantages of good visual effect over the block-based spatial SML method and shift-invariant wavelet method.

Multiresolution Image Representation Using Combined 2-D and 1-D Directional Filter Banks

Abstract: In this paper, effective multiresolution image representations using a combination of 2-D filter bank (FB) and directional wavelet transform (WT) are presented. The proposed methods yield simple implementation and low computation costs compared to previous 1-D and 2-D FB combinations or adaptive directional WT methods. Furthermore, they are nonredundant transforms and realize quad-tree like multiresolution representations. In applications on nonlinear approximation, image coding, and denoising, the proposed filter banks show visual quality improvements and have higher PSNR than the conventional separable WT or the contourlet.

Medical image fusion scheme using complex contourlet transform based on PCA

Abstract: We present an efficient method for the fusion of medical captured images using different modalities that enhances the original images and combines the complementary information of the various modalities. The contourlet transform has mainly been employed as a fusion technique for images obtained from equal or different modalities. The limitation of directional information of dual-tree complex wavelet (DT-CWT) is rectified in dual-tree complex contourlet transform (DT-CCT) by incorporating directional filter banks (DFB) into the DT-CWT. The DT-CCT produces images with improved contours and textures, while the property of shift invariance is retained. To improve the fused image quality, we propose a new method for fusion rules based on principle component analysis (PCA) which depend on frequency component of DT-CCT coefficients (contourlet domain). For low frequency components, PCA method is adopted and for high frequency components, the salient features are picked up based on local energy. The final fusion image is obtained by directly applying inverse dual tree complex contourlet transform (IDT-CCT) to the fused low and high frequency components. The experimental results showed that the proposed method produces fixed image with extensive features on multimodality.

Efficient design of a low redundant Discrete Shearlet Transform

Abstract: Recently, there has been a huge interest in multiresolution representations that also perform a multidirectional analysis. The Shearlet transform provides both a multiresolution analysis (such as the wavelet transform), and at the same time an optimally sparse image-independent representation for images containing edges. Existing discrete implementations of the Shearlet transform havemainly focused on specific applications, such as edge detection or denoising, and were not designed with a low redundancy in mind (the redundancy factor is typically larger than the number of orientation subbands in the finest scale). In this paper, we present a novel design of a Discrete Shearlet Transform, that can have a redundancy factor of 2.6, independent of the number of orientation subbands, and that has many interesting properties, such as shift-invariance and self-invertability. This transform can be used in a wide range of applications. Experiments are provided to show the improved characteristics of the transform.

Efficient IRIS Recognition Through Improvement of Feature Extraction and subset Selection

Abstract: the selection of the optimal feature subset and the classification has become an important issue in the field of iris recognition. In this paper we propose several methods for iris feature subset selection and vector creation. The deterministic feature sequence is extracted from the iris image by using the contourlet transform technique. Contourlet transform captures the intrinsic geometrical structures of iris image. It decomposes the iris image into a set of directional sub-bands with texture details captured in different orientations at various scales so for reducing the feature vector dimensions we use the method for extract only significant bit and information from normalized iris images. In this method we ignore fragile bits. And finally we use SVM (Support Vector Machine) classifier for approximating the amount of people identification in our proposed system. Experimental result show that most proposed method reduces processing time and increase the classification accuracy and also the iris feature vector length is much smaller versus the other methods.

Content Based Medical Image Retrieval by Combining Rotation Invariant Contourlet Features and Fourier Descriptors

Abstract: Designing and modeling methods for medical image search is a challenging task. Content based medical image retrieval, which aims at searching the image database using invariant features, is an important research area for manipulating large amount of medical image databases. This paper focuses on the problem of texture and shape feature extraction. A novel approach by successfully combining rotation invariant contourlet transform and Fourier descriptors is proposed. Rotation invariant contourlet transform is used for texture feature extraction and Fourier descriptor extracts shape features. The retrieval performance of this method is tested using a large medical image database and measured using commonly used performance measurement. Index Terms—CBIR, Rotation invariant contourlet transform, Fourier descriptor, Feature extraction, Similarity measure.

Image quality assessment using contourlet transform

Abstract: We present a novel objective full-reference image quality assessment metric based on multiscale geometric analysis. The multichannel behavior of the human vision system is emulated by contourlet transform, a perceptual subband decomposition. Not only the contrast-masking effect but also the entropy-masking effect is considered to deal with the visual masking issue. In the error pooling stage, the frequency sensitivity of the HVS is investigated. Nonlinear and linear fusion schemes of subband distortion are compared. Extensive validation experiments are performed on two professional image databases, the LIVE database supplied by the University of Texas and the A57 database supplied by Cornell University. Compared with several state-of-the-art image quality metrics, the proposed metric demonstrates improvement on prediction accuracy and robustness.

Improving 2-DE gel image denoising using contourlets.

Abstract: One of the most commonly used methods for protein separation is 2-DE. After 2-DE gel scanning, images with a plethora of spot features emerge that are usually contaminated by inherent noise. The objective of the denoising process is to remove noise to the extent that the true spots are recovered correctly and accurately i.e. without introducing distortions leading to the detection of false-spot features. In this paper we propose and justify the use of the contourlet transform as a tool for 2-DE gel images denoising. We compare its effectiveness with state-of-the-art methods such as wavelets-based multiresolution image analysis and spatial filtering. We show that contourlets not only achieve better average S/N performance than wavelets and spatial filters, but also preserve better spot boundaries and faint spots and alter less the intensities of informative spot features, leading to more accurate spot volume estimation and more reliable spot detection, operations that are essential to differential expression proteomics for biomarkers discovery.

Localized contourlet features in vehicle make and model recognition

Abstract: Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance.

Visible and IR Data Fusion Technique Using the Contourlet Transform

Abstract: In the last few years image fusion has gained considerable attention, where it can provide remarkable outputs for many image applications (\emph{i.e.}, detection of hidden objects). Images with different specifications (resolution, spectral, and spatial) can be fused to produce an output image that combines the best features of all input images. The quality of the output image varies based on the application. In this paper, a new region-based image fusion technique using the Contourlet Transform (CT) is proposed. The presented fusion approach combines the visual information from a visual colored image, and some information about the hidden objects from an IR image. The fused output image is better for human and machine interpretation, where it preserves the original chromaticity of the visual input image. The input images are segmented into small regions more suitable for the proposed algorithm. The segmentation process is performed in the frequency domain. The presented region-based fusion approach is more robust than the traditional pixel-based techniques, where it reduces: the blurring effects, sensitivity to the misregistration problem, and noise effects in the input images. Experimental results demonstrate the capability of the presented fusion technique in detecting hidden weapons and objects. Moreover, the algorithm preserves very high percentage of the input image's spectral components.

Remote Sensing Images Fusion Algorithm Based on Shearlet Transform

Abstract: The shearlet can give an asymptotic optimal representation of edges and contours in images by virtue of it characteristics of good multiresolution and high directionality. In this paper, a novel image fusion strategy is presented for panchromatic high resolution images and multispectral images in shearlet transform domain. The fusion algorithm use the intensity component addition strategy based on LHS transform to preserve spatial resolution and color content. The experimental results demonstrate that the proposed algorithm, compared to intensity-hue-saturation (IHS) transform technique, corresponding wavelet transform-based fusion method, and contourlet transform-based fusion method, can improve spatial resolution and keep spectral information. In addition, the proposed algorithm can extract more useful information from source images, and make the fused image with higher performance in terms of both visual quality and objective evaluation criteria.

A Comparative Study in Wavelets, Curvelets and Contourlets as Feature Sets for Pattern Recognition

Abstract: There have been a number of recent works in computer vision that had used new age multiresolution multidirectional transforms like curvelets and contourlets for face and character recognition. Although these works produced high recognition accuracies they did not provide any comparative study against more well known techniques and hence could not justify the use of these new transforms as against more traditional methods. In this work we will compare the recognition accuracies of the aforesaid two transforms against a very well known multiresolution transform viz. the wavelet transform. this study aims at showing the research community how good or how bad the aforesaid transforms are when compared against wavelets as a feature set for pattern recognition.

Skin Detection Using Contourlet-Based Texture Analysis

Abstract: Detection of skin pixels in arbitrary images is addressed in this paper. We have combined texture and color information to segment skin regions. First, a pixel-based boosted skin detection method is used to locate skin pixels. To further improve the detect performance, skin region texture features are employed using the nonsubsampled contourlet coefficients. For the candidate skin pixels, the set of 8x8 patches around that pixel in all subimages are selected and the feature vector of each patch is extracted. Multilayer perceptron is then utilized to learn features and classify any given input sample. The proposed algorithm has achieved true positive rate of about 82.8% and false positive rate of about 7.6% on the test set that contains 300 images. Experimental results show the effectiveness of the proposed method when compared to the other state-of-the-art texture-based skin segmentation approaches.

ContSteg: Contourlet-Based Steganography Method

Abstract: A category of techniques for secret data communication called steganography hides data in multimedia me-diums. It involves embedding secret data into a cover-medium by means of small perceptible and statistical degradation. In this paper, a new adaptive steganography method based on contourlet transform is presented that provides large embedding capacity. We called the proposed method ContSteg. In contourlet decomposi-tion of an image, edges are represented by the coefficients with large magnitudes. In ContSteg, these coeffi-cients are considered for data embedding because human eyes are less sensitive in edgy and non-smooth re-gions of images. For embedding the secret data, contourlet subbands are divided into 4×4 blocks. Each bit of secret data is hidden by exchanging the value of two coefficients in a block of contourlet coefficients. Ac-cording to the experimental results, the proposed method is capable of providing a larger embedding capacity without causing noticeable distortions of stego-images in comparison with a similar wavelet-based steg-anography approach. The result of examining the proposed method with two of the most powerful steganaly-sis algorithms show that we could successfully embed data in cover-images with the average embedding ca-pacity of 0.05 bits per pixel.

Robust feature points extraction for image registration based on the nonsubsampled contourlet transform

Abstract: In this letter, a new feature points extraction method based on the nonsubsampled contourlet transform (NSCT) is proposed for image registration. The primary motivation of this work is to determine the effectiveness of the NSCT transform in extracting feature points for image registration. Preliminary experimental results show that the registration accuracy and robustness of the proposed algorithm is acceptable and very promising, and confirm the success of the proposed NSCT-based feature points extraction approach.

An efficient method for human face recognition using nonsubsampled contourlet transform and support vector machine

Abstract: To improve the recognition rate in different conditions, a multiscale face recognition method based on nonsubsampled contourlet transform and support vector machine is proposed in this paper. Firstly, all face images are decomposed by using nonsubsampled contourlet transform. The contourlet coefficients of low frequency and high frequency in different scales and various angles will be obtained. Most significant information of faces is contained in coefficients, which is important for face recognition. Then, the combinations of coefficients are applied as study samples to the support vector machine classifiers. Finally, the decomposed coefficients of testing face image are used to test classifiers, then face recognition results are obtained. The experiments are performed on the YaleB database and the Cambridge University ORL database. The results indicate that the method proposed has performs better than the wavelet-based method. Compared with the wavelet-based method, the proposed method can make the best recognition rates increase by 2.85% for YaleB database and 1.87% for ORL database, respectively. Our method is also suitable for other face databases and appears to work well.