Showing papers on "Contourlet published in 2008"

An Efficient Pan-Sharpening Method via a Combined Adaptive PCA Approach and Contourlets

Abstract: High correlation among the neighboring pixels both spatially and spectrally in a multispectral image makes it necessary to use an efficient data transformation approach before performing pan-sharpening. Wavelets and principal component analysis (PCA) methods have been a popular choice for spatial and spectral transformations, respectively. Current PCA-based pan-sharpening methods make an assumption that the first principal component (PC) of high variance is an ideal choice for replacing or injecting it with high spatial details from the high-resolution histogram-matched panchromatic (PAN) image. This paper presents a combined adaptive PCA-contourlet approach for pan-sharpening, where the adaptive PCA is used to reduce the spectral distortion and the use of nonsubsampled contourlets for spatial transformation in pan-sharpening is incorporated to overcome the limitation of the wavelets in representing the directional information efficiently and capturing intrinsic geometrical structures of the objects. The efficiency of the presented method is tested by performing pan-sharpening of the high-resolution (IKONOS and QuickBird) and the medium-resolution (Landsat-7 Enhanced Thematic Mapper Plus) datasets. The evaluation of the pan-sharpened images using global validation indexes reveal that the adaptive PCA approach helps reducing the spectral distortion, and its merger with contourlets provides better fusion results.

Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain

Abstract: Nonsubsampled contourlet 变换(NSCT ) 为图象提供灵活 multiresolution， anisotropy，和方向性的扩大。与原来的 contourlet 变换相比，它是移动不变的并且能在奇特附近克服 pseudo-Gibbs 现象。脉搏联合了神经网络(PCNN ) 是一个视觉启发外皮的神经网络并且由全球联合和神经原的脉搏同步描绘。它为图象处理被证明合适并且成功地在图象熔化采用。在这份报纸， NSCT 与 PCNN 被联系并且在图象熔化使用了充分利用他们的特征。在 NSCT 领域的空间频率是输入与大开火的时间在 NSCT 领域激发 PCNN 和系数作为熔化图象的系数被选择。试验性的结果证明建议算法超过典型基于小浪，基于 contourlet，基于 PCNN，并且 contourlet-PCNN-based 熔化算法以客观标准和视觉外观。

The Shiftable Complex Directional Pyramid—Part I: Theoretical Aspects

Abstract: This paper presents an over-complete multiscale decomposition by combining the Laplacian pyramid and the complex directional filter bank (DFB). The filter bank is constructed in such a way that each complex directional filter is analytical using the dual-tree structure of real fan filters. Necessary and sufficient conditions in order for the resulting multirate filter bank to be shift-invariant in energy sense (shiftability) are derived in terms of the magnitude and phase responses of these filters. Their connection to 2D Hilbert transform relationship is established. The proposed transform possesses several desirable properties including multiresolution, arbitrarily high directional resolution, low redundant ratio, and efficient implementation.

Contourlet-based image adaptive watermarking

Abstract: In the contourlet transform (CT), the Laplacian pyramid (LP) decomposes an image into a low-frequency (LF) subband and a high-frequency (HF) subband. The LF subband is created by filtering the original image with 2-D low-pass filter. However, the HF subband is created by subtracting the synthesized LF subband from the original image but not by 2-D high-pass filtering the original image. In this paper, we propose a contourlet-based image adaptive watermarking (CIAW) scheme, in which the watermark is embedded into the contourlet coefficients of the largest detail subbands of the image. The transform structure of the LP makes the embedded watermark spread out into all subbands likely in which the LF subbands are included when we reconstruct the watermarked image based on the watermarked contourlet coefficients. Since both the LF subbands and the HF subbands contain watermarking components, our watermarking scheme is expected to be robust against both the LF image processing and the HF image processing attacks. The corresponding watermarking detection algorithm is proposed to decide whether the watermark is present or not by exploiting the unique transform structure of LP. With the new proposed concept of spread watermark, the watermark is detected by computing the correlation between the spread watermark and the watermarked image in all contourlet subbands fully. The proposed CIAW scheme is particularly superior to the conventional watermarking schemes when the watermarked image is attacked by some image processing methods, which destroy the HF subbands, thanks to the watermarking components preserved in the LF subbands. Experimental results show the validity of CIAW in terms of both the watermarking invisibility and the watermarking robustness. In addition, the comparison experiments prove the high-efficiency of CIAW again.

SAR Image Filtering Via Learned Dictionaries and Sparse Representations

Abstract: In the last decade there has been a growing interest in the study of sparse representation of signals. In particular, many new multiscale image representations in a geometric space have been proposed (Curvelets, Ridgelets, Contourlets, etc.). Instead of using a fixed transformation, an alternative approach is to build a sparse dictionary from the signal itself. In the present work, we propose a novel approach for speckle noise reduction in SAR images using a sparse and redundant representation over trained dictionaries. In this approach, an adaptive dictionary composed of image patches (called atoms) is learned from the image so that it constitutes a sparse representation of the image content. This learning process, called K-SVD, is efficiently performed using an Orthogonal Matching Pursuit (OMP) and a Singular Value Decomposition (SVD). This new approach is effective in removing white additive Gaussian noise despite the fact that elements of the dictionary are learned from the noisy image, the algorithm is converging toward meaningful atoms that are already showing a reduction in noise level.

Multi-scale multi-camera adaptive fusion with contrast normalization

Abstract: A computer implemented method for fusing images taken by a plurality of cameras is disclosed, comprising the steps of: receiving a plurality of images of the same scene taken by the plurality of cameras; generating Laplacian pyramid images for each source image of the plurality of images; applying contrast normalization to the Laplacian pyramids images; performing pixel-level fusion on the Laplacian pyramid images based on a local salience measure that reduces aliasing artifacts to produce one salience-selected Laplacian pyramid image for each pyramid level; and combining the salience-selected Laplacian pyramid images into a fused image. Applying contrast normalization further comprises, for each Laplacian image at a given level: obtaining an energy image from the Laplacian image; determining a gain factor that is based on at least the energy image and a target contrast; and multiplying the Laplacian image by a gain factor to produce a normalized Laplacian image.

Robust Digital Watermarking Scheme using Contourlet Transform

Abstract: Summary A novel oblivious and highly robust watermarking scheme using Multiple Descriptions (MD) and Quantization Index Modulation (QIM) of the host image is presented in this paper. The watermark is embedded in the Discrete Contourlet Transform domain (CT). Discrete Countourlet Transform (CT) is able to capture the directional edges and contours superior to Discrete Wavelet Transform (DWT). Watermark embedding is done at two stages for achieving robustness to various attacks. This algorithm is highly robust for different attacks on the watermarked image and superior in terms of Peak Signal to Noise Ratio (PSNR) and Normalized Cross correlation (NC). here the part of summary.

Vehicle Recognition Using Contourlet Transform and SVM

Abstract: This paper proposes the performance of a new algorithm for vehicles recognition system. This recognition system is based on extracted features on the performance of image's Contourlet transform & achieving standard deviation of Contourlet coefficients matrix in different subbands & various directions. This paper presents the application of three different types of classifiers to the vehicle recognition. They include support vector machine (one versus one), k nearest-neighbor and support vector machine (one versus all). In addition, the proposed recognition system is obtained by using different subbands information as feature vector. So, we could clarify the most important subbands in aspect of having useful information. The performed numerical experiments for vehicles recognition have shown the superiority of Contourlet and standard deviation preprocessing, which are associated with the support vector machine structure (one versus one). The results of this test show, the right recognition rate of vehicle's model in this recognition system, at the time of using total subbands information numbers 3&4 Contourlet coefficients matrix is about 99%. We've gathered a data set that includes 300 images from 5 different classes of vehicles. These 5 classes of vehicles include of: PEUGEOT206, PEUGEOT405, Pride, RENAULT and Peykan. We 've examined 230 pictures as our train data set and 70 pictures as our test data set.

Context adaptive image denoising through modeling of curvelet domain statistics

Abstract: We perform a statistical analysis of curvelet coefficients, distinguishing between two classes of coefficients: those that contain a significant noise-free component, which we call the “signal of interest,” and those that do not. By investigating the marginal statistics, we develop a prior model for curvelet coefficients. The analysis of the joint intra- and inter-band statistics enables us to develop an appropriate local spatial activity indicator for curvelets. Finally, based on our findings, we present a novel denoising method, inspired by a recent wavelet domain method called ProbShrink. The new method outperforms its wavelet-based counterpart and produces results that are close to those of state-of-the-art denoisers.

Compressed sensing MRI based on nonsubsampled contourlet transform

Abstract: How to reduce acquisition time is very important in magnetic resonance imaging (MRI). Compressed sensing MRI emerges recently to suppress the aliasing when undersampling k-space data is employed. However, typical sparse transform for compressed sensing MRI ever used is wavelet, which only captures limited directional information with decay rate M1. In this paper, we introduce contourlet into compressed sensing to obtain a sparse expansion for smooth contours with decay rate C(logM)3M2 and employ nonsubsampled contourlet to increase the redundancy of basis for magnetic resonance images. We propose compressed sensing MRI based on nonsubsampled contourlet transform (NSCT). Experimental results demonstrate that NSCT outperforms wavelet on suppressing the aliasing and improves the visual appearance of magnetic resonance images.

Adaptive steganography method based on contourlet transform

Abstract: In this paper, a new adaptive steganographic scheme based on contourlet transform is presented that provides large embedding capacity. In this method, embedding is done in contourlet transform domain. The contourlet coefficients with larger magnitude that correspond to the edges are selected for embedding. This selection is due to less sensitivity of human eyes to non-smooth regions. Each bit of secret data is embedded by exchanging the value of two coefficients in a 4 times 4 block of a contourlet subband. The proposed method is examined with two strong steganalysis algorithms and the results show that we could successfully embed data in a cover image with the capacity of 0.05 bits per pixel. Experiments and comparative studies showed the effectiveness of the proposed technique in generating stego images. In addition, its superiority is shown by comparison with a similar wavelet-based steganography approach.

Contourlet-Based Feature Extraction with PCA for Face Recognition

Abstract: Face recognition is still a challenging task because face images can vary considerably in terms of facial expressions, lighting conditions, ... etc. It is commonly known that the use of multiresolution filter banks improve the recognition accuracy of image based biometric systems. In this paper, we propose to investigate the usefulness of the multiscale and directionality properties of the contourlet transform with a view to extract more discriminant features in order to further enhance the performance of the well known principal component analysis method when applied to face recognition. The proposed method has been extensively assessed using two different databases: the YALE Face Database and the FERET Database. A series of experiments have been carried out and a comparative study suggests the efficiency of the Contourlet Transform in enhancing the classification rates of a number of known face recognition algorithms.

Pavement Distress Detection Based on Nonsubsampled Contourlet Transform

Abstract: Automatic recognition of road distresses has been a hot topic since it reduces economic loses before cracks and potholes become too severe. However, weak information of road distress and computing complexity make it difficult to detect road distress effectively. In this paper, we describe pavement distress detection based on nonsubsampled contourlet transform (NSCT). The NSCT is based on a nonsubsampled pyramid structure and nonsubsampled directional filter banks. The coefficients in different scales and different directions are obtained by image decomposition using the nonsubsampled contourlet transform. After the enhancement of weak information and repress noise through adjustment the coefficients in NSCT subbands with proposed algorithm in this paper then reconstruction of these coefficients, pavement distress detection is implemented. Compared with other algorithms, this approach can get better effect especial for weak information. Experimental results proved that the proposed detection was an effective method for the pavement distress image in the practical application, which could inspect the weak object accurately.

Pseudo-Fisherface method for single image per person face recognition

Abstract: The problem of recognizing a face from a single sample available in a stored dataset is addressed. A new method of tackling this problem by using the Fisherface method on a generic dataset is explored. The recognition scheme is also extended to multiscale transform domains like wavelet, curvelet and contourlet. The proposed method in the transform domain shows better recognition errors than the SPCA algorithm and Eigenface selection method, both of which are specially tailored for recognizing faces from single samples.

Fusion of Remote Sensing Images Using Contourlet Transform

Abstract: Image fusion is a process of producing a single image from a set of input images. Recently, the wavelet transform (WT) has been widely used in image fusion. However, the Contourlet transform give better results because it represents edges better than the wavelets transform. In this paper, fusion algorithms based on the contourlet transform are proposed. These algorithms are tested and compare to an existing similar algorithm using Synthesized and QuickBird images. The experimental results show the superiority of the proposed algorithms over the existing contourlet-based one.

An enhanced segmentation of blood vessels in retinal images using contourlet

Abstract: Retinal images acquired using a fundus camera often contain low grey, low level contrast and are of low dynamic range. This may seriously affect the automatic segmentation stage and subsequent results; hence, it is necessary to carry-out preprocessing to improve image contrast results before segmentation. Here we present a new multi-scale method for retinal image contrast enhancement using Contourlet transform. In this paper, a combination of feature extraction approach which utilizes Local Binary Pattern (LBP), morphological method and spatial image processing is proposed for segmenting the retinal blood vessels in optic fundus images. Furthermore, performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP) is investigated in the classification section. The performance of the proposed algorithm is tested on the publicly available DRIVE database. The results are numerically assessed for different proposed algorithms.

A New Visually Imperceptible and Robust Image Watermarking Scheme in Contourlet Domain

Abstract: This paper proposes a new blind spread spectrum image watermarking method in Contourlet domain, where watermark is embedded through PN sequences in the selected Contourlet coefficients of the cover image. Data embedding is performed in selected sub bands, providing higher resiliency through better spectrum spreading compared to other sub bands. It has been observed that, in comparison with other Contourlet based and wavelet based methods, the proposed method leads to higher imperceptibility and robustness against several common watermarking attacks such as compression, adding noise, filtering and scaling. This property of the proposed method is thanks to its capability in directional selectivity.

CT and MRI Image Fusion Based on Contourlet Using a Novel Rule

Abstract: Contourlet can efficiently represent image and be used as image fusion method. When fusion scheme is selected, fusion performance mainly rests on fusion rules. Fusion rules based on region energy is often used in multiresolution analysis (MRA) image fusion scheme. It is more robust and less sensitive to noise. But small region window is often not the best one, and large window blurs the gap between edges and none edges. This paper proposed a novel fusion rule based on weighted region energy. Each pixel in the window has a weight in inverse proportion to the distance between it and the current fusion pixel. And this rule is applied to both lowpass and bandpass coefficients of contourlet transform. Experiments show that the new fusion scheme has better performance than the fusion methods based on traditional region.

Contourlet Based Image Compression for Wireless Communication in Face Recognition System

Abstract: This paper proposes to use Contourlet transform for image compression and feature extraction for wireless face recognition system. The properties of face images and face recognition techniques are incorporated into the design of wireless transmission for such a system. The reasons for utilizing contourlet transform are two-folded. Firstly, in face recognition, the edge information is crucial in deriving features, and the edges within a face image are not just horizontal or vertical. When the coefficients are transmitted through the fading channel, the reconstruction from the Stein-thresholded noisy coefficients by contourlet achieves less mean square error than by wavelet. Secondly, when the network resources limit the transmission of full-set coefficients, the lower band coefficients can serve as a scaled-down version of the face image, for a coarser face recognition as screening. A prioritized transmission of the coefficients take full advantage of the wireless channel. Simulation shows that the wireless face recognition system works as well as a wired one, while gaining the cost efficiency, and the flexibility in deployment. An interesting phenomenon is discovered on FERET database that when the transmission error rate is increased linearly, the recognition performance degradation is not linear; instead, the performance stays the same for a large range of error rates, which illustrates that contourlet based face recognition system can tolerate the transmission error up to some threshold.

A Steganalysis Method Based on Contourlet Transform Coefficients

Abstract: Steganalysis is a technique to detect the presence of hidden embedded information in a given data. Each steganalyzer is composed of feature extraction and feature classification components. Using features that are more sensitive to data hiding yields higher success in steganalysis. The present paper offers a new universal approach to steganalysis that uses statistical moments of contourlet coefficients as features for analysis. A non-linear SVM classifier is used to classify cover and stego images. The effectiveness of the proposed method is demonstrated by extensive 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.

Combined Wavelet and Contourlet Denoising of SAR Images

Abstract: The nonsubsampled contourlet transform (NSCT) is a new image representation approach that has sparser representation at both spatial and directional resolution and thus captures smooth contours in images On the other hand, wavelet transform has sparser representation of homogeneous areas. In this paper, three combinations of undecimated wavelet and nonsubsampled contourlet transforms will be used for denoising of SAR images. Two of the methods use the wavelet transform to denoise homogeneous areas and the nonsubsampled contourlet transform to denoise areas with edges. The segmentation between homogeneous areas and areas with edges is done by using total variation segmentation. The third method is a linear averaging of the two denoising methods. A thresholding in the wavelet and contourlet domain is done by non-linear functions which are adapted for each selected subband. The non-linear functions are based on sigmoid functions. Simulation results suggested that these denoising schemes achieve good and clean images.

A Comparative study between wavelet and Contourlet Transform Features for Textural Image Classification

Abstract: Contourlet transform is a new two-dimensional extension of the wavelet transforms using multiscale and directional filter banks. In this paper, the effectiveness of the features obtained from the contourlet transform is investigated and is compared with the wavelet transform features for image texture classification. We specially focused on image acquisition conditions that an image from one scene may be acquired with different illumination, scale, direction, distance and slope. It is shown that the accuracy of the contourlet transform features in such conditions is more than that of the wavelet transform. However, wavelet transform is still applicable in many texture classification tasks.

Region-based fusion of infrared and visible images using nonsubsampled contourlet transform

Abstract: With the nonsubsampled contourlet transform (NSCT), a novel region-segmentation-based fusion algorithm for infrared (IR) and visible images is presented. The IR image is segmented according to the physical features of the target. The source images are decomposed by the NSCT, and then, different fusion rules for the target regions and the background regions are employed to merge the NSCT coefficients respectively. Finally, the fused image is obtained by applying the inverse NSCT. Experimental results show that the proposed algorithm outperforms the pixel-based methods, including the traditional wavelet-based method and NSCT-based method.

Contourlet-Based Feature Extraction on Texture Images

Abstract: According to the directional property and coefficients energy feature in contourlet decomposition, we proposed a new algorithm which is adapted to extract the rotated texture?s features. By using this algorithm, we can take good advantage of directional information in contourlet decomposition by different decomposition level. With the extracted feature vectors, we can easily distinguish different texture in standard Brodatz texture database with high classification accuracy.

Palmprint Identification Using Contourlet Transform

Abstract: Palmprint based personal verification has gained preference over other biometric modalities due to its ease of acquisition, high user acceptance and reliability. This paper presents a novel palmprint based identification approach which uses the textural information available on the palmprint by employing the Contourlet Transform (CT). After establishing the region of interest (ROI), the two dimensional (2-D) spectrums is divided into fine slices, using iterated directional filterbanks. Next, directional energy component for each block from the decomposed subband outputs is computed. The proposed algorithm captures both local and global details in a palmprint as a compact fixed length palm code. Palmprint matching is then performed using normalized Euclidean distance classifier. The proposed algorithm is tested on a total of 7752 palm images, acquired from the standard database of Polytechnic University of Hong Kong. The experimental results demonstrated the feasibility of the proposed system by exhibiting genuine acceptance rate of 88.91%, decidability index of 2.7748 and equal ierror rate of 0.2333%.