Nonsubsampled Contourlet Transform Research Articles

An Improved Method on Non-subsampled Contourlet Transform Ultrasonic Image Denoising

The non-subsampled contourlet transform (NSCT) is a good approach in capturing the detail of images, so that it has a wide range of applications in image denoising. In order to improve the denoising effect of NSCT method of medical ultrasound images, the local intuitionistic fuzzy entropy (LIFE) theory is employed in this work to suppress the noise in the high-frequency coefficients of NSCT. On combination with the LIFE theory and the directionality of the coefficients’ distribution of NSCT, the directional local intuitionistic fuzzy entropy (DLIFE) method has been put forward to calculate the corresponding noise suppression factor to remove the noise in the high-frequency coefficients of NSCT. The experiments on both Barbara image and medical ultrasound image show a better denoising effect than the original NSCT soft threshold method and the wavelet soft threshold method.

A Hybrid Multimodal Medical Image Fusion Technique for CT and MRI Brain Images

Estimating the type, size, location and spread of a brain tumor is vital in the diagnosis and treatment of brain cancer. Fused CT and MRI brain images assist in faster detection and diagnosis of brain tumors. They provide superior results in comparison to individual CT or MRI images. Multiscale transforms (MSTs) are widely used in fusing multimodal images like CT and MRI. However, they have a few drawbacks like reduced contrast, poor edge detection, redundancy and high computation time. This article describes how MSTs coupled with sparse representation (SR) aims to overcome the drawbacks. Non-Subsampled Contourlet Transform (NSCT) is widely used on MSTs for fusing multifocal images. Therefore, a novel technique using NSCT and SR is proposed for better quality fused CT and MRI images. The experimental results show superior performance.

Infrared Aerial Small Target Detection with NSCT and Two-Dimensional Property Histogram

A novel algorithm is presented based on non-subsampled contourlet transform (NSCT) and two dimension property histogram in order to realize the aerial small target detection of infrared imaging under complex background. First, this method transforms the infrared image from space domain to NSCT domain. In high frequency bandpass domain, this method describes the sub-band coefficients according to Gaussian scale mixture model based on Bayesian estimation and estimates the center coefficient with the local neighbor’s in order to predict the high frequency background. On the other hand, this method predicts the low frequency background with self-adaption median filter in low frequency lowpass domain. Subsequently, the reversing NSCT is done and the complex background is estimated. By means of subtracting the estimated background image from the source image, the complex background is suppressed and the outstanding small target is acquired. Second, constructing the target’s property set according to the priori knowledge, this method defines the corresponding two-dimensional property histogram which is applied into calculating the segmenting threshold on basis of the maximum entropy method. Subsequently, the infrared image whose complex background is suppressed will be segmented into binary image by the threshold. Finally, infrared small target is detected by the pipeline filter algorithm which makes use of the relativity of the target movement between frames. The experimental results prove the presented method’s effectiveness which can detect the small target whose signal noise ratio (SNR) value is above 2 steadily.

An effective image fusion algorithm based on grey relation of similarity and morphology

Considering the problems of the limited energy in wireless multi-media sensor networks (WMSNs) and the focused regions discontinuity of the fused image obtained using traditional multi-scale analysis tools (MST)-based methods, an effective multi-focus image fusion algorithm is proposed in this paper. In this method, the original fused image is obtained based on wavelet transform where the low-frequency coefficients are fused by average scheme, whereas the high-frequency coefficients are fused by the proposed merging rule consisting of the grey relation analysis of similarity and local area energy. Then, grey absolute relation analysis is again utilized as measurement indicator to estimate the similarities between the initial fused image and source images, during which the initial map is acquired and then corrected by the mathematical morphological opening and closing. Finally, the fused image is obtained with the guidance of the corrected map, namely the decision map. Experiment results demonstrate that the fused image using the proposed algorithm is more continuous in focused region and more similar to source images in brightness compared with state-of-art multi-focus image fusion algorithms, such as Curvelet transform, lifting stationary wavelet transform (LSWT), non-subsampled contourlet transform (NSCT) and non-subsampled shearlet transform (NSST). Meanwhile, the proposed method shows better superiority in term of the computational complexity.

Local energy match based non-sub sampled contourlet Transform for multi modal medical image fusion

Image processing technology requires moreover the full image or the part of image which is to be processed from the user’s point of view like the radius of object etc. The main purpose of fusion is to diminish dissimilar error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the concerned objects is more important than extra information. So preserving the edge features of the image is worth for investigating the image fusion. The image with higher contrast contains more edge-like features. Here we propose a new medical image fusion scheme namely Local Energy Match NSCT based on discrete contourlet transformation, which is constructive to give the details of curve edges. It is used to progress the edge information of fused image by dropping the distortion. This transformation lead to crumbling of multimodal image addicted to finer and coarser details and finest details will be decayed into unusual resolution in dissimilar orientation. The input multimodal images namely CT and MRI images are first transformed by Non Sub sampled Contourlet Transformation (NSCT) which decomposes the image into low frequency and high frequency elements. In our system, the Low frequency coefficient of the image is fused by image averaging and Gabor filter bank algorithm. The processed High frequency coefficients of the image are fused by image averaging and gradient based fusion algorithm. Then the fused image is obtained by inverse NSCT with local energy match based coefficients. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Correlation Coefficient parameters are used in this work .

Infrared and visible image fusion based on NSCT and stacked sparse autoencoders

To integrate the infrared object into the fused image effectively, a novel infrared (IR) and visible (VI) image fusion method by using nonsubsampled contourlet transform (NSCT) and stacked sparse autoencoders (SSAE) is proposed. Firstly, the IR and VI images are decomposed into low-frequency subbands and high-frequency subbands by using NSCT. Secondly, SSAE is performed on the low frequency subband of IR image to calculate the object reliabilities (OR) of the low frequency subband coefficients. Subsequently, an adaptive multi-strategy fusion rule based on OR is designed for the fusion of low frequency subbands and a choose-max fusion rule with the absolute values of high frequency subband coefficients are employed for the fusion of high frequency subbands. Experimental results show the proposed method is superior to the conventional methods in highlighting the infrared objects as well as keeping the background information in VI image.

Multi-focus image fusion based on block matching in 3D transform domain

Fusion methods based on multi-scale transforms have become the mainstream of the pixel-level image fusion. However, most of these methods cannot fully exploit spatial domain information of source images, which lead to the degradation of image. This paper presents a fusion framework based on block-matching and 3D (BM3D) multi-scale transform. The algorithm first divides the image into different blocks and groups these 2D image blocks into 3D arrays by their similarity. Then it uses a 3D transform which consists of a 2D multi-scale and a 1D transform to transfer the arrays into transform coefficients, and then the obtained low- and high- coefficients are fused by different fusion rules. The final fused image is obtained from a series of fused 3D image block groups after the inverse transform by using an aggregation process. In the experimental part, we comparatively analyze some existing algorithms and the using of different transforms, e.g. non-subsampled Contourlet transform (NSCT), non-subsampled Shearlet transform (NSST), in the 3D transform step. Experimental results show that the proposed fusion framework can not only improve subjective visual effect, but also obtain better objective evaluation criteria than state-of-the-art methods.

DC cable feature extraction based on the PD image in the non-subsampled contourlet transform domain

A new method to extract the features of direct current (DC) cross linked polyethylene (XLPE) cable partial discharge (PD) image based on non-subsampled contourlet transform (NSCT) is proposed in this paper. Four types of PD images are obtained from the artificially designed cable insulation defects. The features include Shannon entropy, Renyi entropy, Euclidean distance, and Mahalanobis distance of the optimized subband coefficients by NSCT. The optimized subband coefficients are selected by the immune algorithm optimized affinity propagation clustering method. The back propagating neural network, k-nearest neighbor, support vector machine and decision tree algorithms are used to classify the extracted features. The experimental results show that the proposed method can be applied to the feature extraction of PD signals in DC XLPE cables. Compared with the wavelet method, the recognition rate of the method is improved by more than 16.80% under the same classifier. It is found that the type of defect within insulation can be classified efficiently with the features extracted by the proposed method with a reasonable degree of accuracy.

Poisson-Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain.

The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson–Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson–Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods.

A Medical Image Fusion Method Based on NSCT and PCNN with Neighborhood Stimulation

In order to better guide clinical diagnosis and treatment, this paper proposes medical image fusion method based on NSCT (nonsubsampled contourlet transform) and PCNN (pulse coupled neural network) with neighborhood stimulation. A fusion rule based on PCNN with neighborhood standard deviation stimulation is adopted in low frequency sub-band coefficient. Through the application of the PCNN simplified model, the PCNN model is stimulated by neighborhood standard deviation according to human visual system. The low frequency sub-band coefficient is determined by firing times. When choosing the bandpass directional sub-band coefficients, a fusion rule based on region gradient energy is adopted according to directional characteristics of NSCT. The experiment results show that the proposed method provides abundant details and edge information over conventional methods in achieving the better quality of fusion.

Weibull statistical modeling for textured image retrieval using nonsubsampled contourlet transform

In this paper, we proposed a new framework for textured image retrieval, which is based on Weibull statistical distribution and nonsubsampled contourlet transform. Firstly, the image is decomposed into one lowpass subband and several highpass subbands by using nonsubsampled contourlet transform (NSCT). Secondly, Weibull probability distribution is employed to describe the statistical characteristics of the highpass NSCT coefficients, and the Weibull model parameters are utilized to construct a compact texture image feature space. Finally, image similarity measurement is accomplished by using closed-form solutions for the Kullback–Leibler divergences between the Weibull statistical models. Experimental results demonstrate the high efficiency of our textured image retrieval scheme, which can provide better retrieval rates and lower computational cost, in comparison with the state-of-the-art approaches recently proposed in the literature.

Multi-focus image fusion combining focus-region-level partition and pulse-coupled neural network

Multi-scale transforms (MST)-based methods are popular for multi-focus image fusion recently because of the superior performances, such as the fused image containing more details of edges and textures. However, most of MST-based methods are based on pixel operations, which require a large amount of data processing. Moreover, different fusion strategies cannot completely preserve the clear pixels within the focused area of the source image to obtain the fusion image. To solve these problems, this paper proposes a novel image fusion method based on focus-region-level partition and pulse-coupled neural network (PCNN) in nonsubsampled contourlet transform (NSCT) domain. A clarity evaluation function is constructed to measure which regions in the source image are focused. By removing the focused regions from the source images, the non-focus regions which contain the edge pixels of the focused regions are obtained. Next, the non-focus regions are decomposed into a series of subimages using NSCT, and subimages are fused using different strategies to obtain the fused non-focus regions. Eventually, the fused result is obtained by fusing the focused regions and the fused non-focus regions. Experimental results show that the proposed fusion scheme can retain more clear pixels of two source images and preserve more details of the non-focus regions, which is superior to conventional methods in visual inspection and objective evaluations.

An Adaptive Sub Band Filtering Based Medical Image Fusion Through Non-subsampled Contourlet Transform

An Adaptive Sub Band Filtering Based Medical Image Fusion Through Non-subsampled Contourlet Transform

Image Adaptive Denoising Based on Nonsubsampled Contourlet Transform

In this paper, researching the correlation property of congenetic subband coefficients of Nonsubsampled Contourlet Transform (NSCT), and proposing a new adaptive denoising theory. This theory combines the excellent shift-invariant of NSCT with the correlation property of congenetic subband coefficients in image denoising. The correlation property contains the entire relational correlation value of the congenetic subband coefficients, and the grey correlation value in single coefficient of the congenetic subband coefficients of the NSCT. According to the correlation property of congenetic subband coefficients, the new algorithm can automatically identify the strong edges, weak edges and noise in the noisy image, and then it can filter the noise and preserve the strong edges and weak edges at certain degree. The experiment results are provided to compare with the elegant non-local means method, which show that the theory proposed in this paper has good effect.

Depth Image Enhancement and Detection on NSCT and Fractional Differential

For enhancing the low contrast and detecting the noise in depth image from Kinect sensor, the Non-Subsampled Contourlet Transform (NSCT) and non-linear fractional differential are studied in this paper. Firstly, on the basis of the NSCT’s advantages, the depth image is decomposed into low frequency and high frequency. The low frequency component is applied to enhance depth image using adaptive scale Retinex, and the scale parameter is adjusted by local mean and standard deviation. The high frequency component is calculated by Non-Local-Means operator to preserve the texture detail. The final enhanced image can be achieved by inverse NSCT. Secondly, the fractional differential theory is studied to accomplish noise detection. The experimental results show that the proposed method can enhance the depth image and detect noise effectively.

Fusion of Infrared and Visible Images based on NSCT and Modified PCNN

For purpose of improving the quality of multi-source fusion images, this paper introduces a fusion method of infrared and visible images based on the combination of the non-subsampled contourlet transform (NSCT) and the improved pulse coupled neural network model (PCNN). To begin with, registered images are decomposed by NSCT, the low frequency and high frequency sub-bands are also obtained. Then, the energy of Laplacian (SML) is used for the link intensity of PCNN, computing spatial frequency (SF) with a low frequency sub-band, and the gradient energy (EOG) with a high frequency sub-band. By means of treating them as external input of PCNN. Then fused coefficients are selected by sum of ignition output amplitude maximum, a fused image is finally obtained by inverse NSCT. Experimental results show that the fusion image has a high definition and effectively preserves information of the source image.

Image compression-based multiple description transform coding using NSCT and OMP approximation

In this paper, we present a novel multiple description transform image coding architecture, which uses an attractive transform called non-subsampled contourlet transform (NSCT). It combines NSCT and orthogonal matching pursuit algorithm (OMP) to give a sparse representation of images, aiming at solving the compression problem due to the redundancy property of NSCT. In this way, OMP turns to give a solution to remove the redundancies. We evaluate the performance of our image coder in the case of four descriptions that are dispatched over different channels. The experimentations show that the proposed method is efficient and the potential using NSCT than DWT in multiple description image coding, is evaluated by PSNR in each case of packet loss, where every description can reconstruct the image with acceptable fidelity, the later is much better if all descriptions are available.

Image compression-based multiple description transform coding using NSCT and OMP approximation

In this paper, we present a novel multiple description transform image coding architecture, which uses an attractive transform called non-subsampled contourlet transform (NSCT). It combines NSCT and orthogonal matching pursuit algorithm (OMP) to give a sparse representation of images, aiming at solving the compression problem due to the redundancy property of NSCT. In this way, OMP turns to give a solution to remove the redundancies. We evaluate the performance of our image coder in the case of four descriptions that are dispatched over different channels. The experimentations show that the proposed method is efficient and the potential using NSCT than DWT in multiple description image coding, is evaluated by PSNR in each case of packet loss, where every description can reconstruct the image with acceptable fidelity, the later is much better if all descriptions are available.

The NSCT-HMT Model of Remote Sensing Image Based on Gaussian-Cauchy Mixture Distribution

The nonsubsampled Contourlet transform (NSCT) not only retains the characteristics of Contourlet transform, but also has the good characteristic of shift-invariance, which plays a significant role in denoising, fusion, and segmentation of texture-rich images. The NSCT not only retains the properties of Contourlet transform, but also has the important property of shift-invariance, which plays a significant role in image processing, such as denoising, fusion, and segmentation of texture-rich images. This paper proposes a Gaussian-Cauchy mixture distribution-based NSCT hidden Markov tree model (GC-NSCT-HMT). The specific form of Gaussian-Cauchy mixture distribution is determined by the kurtosis of the NSCT coefficients in each subband. First, we study the probability density distribution of the remote sensing image NSCT coefficients and then propose the Gaussian-Cauchy mixture distribution, which can adaptively adjust according to the statistical property of NSCT coefficients through a balance function. Experimental results show that the proposed mixture distribution can achieve a good imitative effect to the NSCT coefficients. Second, we study the marginal statistical property and the joint statistical property of the NSCT coefficients, the persistence and aggregation properties of them are also studied in depth. We find that the ‘father’ NSCT coefficient can transfers to its son coefficients through a tree structure. Third, we combine the above conclusions with the hidden Markov tree model (HMT) and the GC-NSCT-HMT model is proposed. Finally, we apply our model to remote sensing image denoising. The subjective and objective experimental results demonstrate the feasibility of the proposed method.

DECISION BASED FUZZY LOGIC APPROACH FOR MULTIMODAL MEDICAL IMAGE FUSION IN NSCT DOMAIN

Image fusion is used to reduce the redundancy and increases the needed information in the processed image from two or more input images that have different information generated by different sources. The output image has more information and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion methods basically accept only registered images to produce a high quality fused single image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in medical applications. In this paper, we proposed an image fusion algorithm based on decision approach and NSCT to improve the future resolution of the images. In this, images will be segmented into regions and decomposed into sub-images and then processed using Fuzzy Logic, the information fusion is performed using these images under the certain criteria such as non subsampled contourlet transform (NSCT) and certain fusion rules such as Fuzzy Logic, and finally these sub-images are reconstructed into the resultant image with plentiful information. The various metrices entropy, mutual information (MI) and Fusion Quality are calculated to compare the results. The proposed method is compared both subjectively as well as objectively with the other image fusion methods. The experimental results show that the proposed method is better than other fusion methods and increases the quality and PSNR of fused image.