Nonsubsampled Contourlet Transform Domain Research Articles

Stripe artifact elimination based on nonsubsampled contourlet transform for light sheet fluorescence microscopy.

Stripe artifacts, caused by high-absorption or high-scattering structures in the illumination light path, are a common drawback in both unidirectional and multidirectional light sheet fluorescence microscopy (LSFM), significantly deteriorating image quality. To circumvent this problem, we present an effective multidirectional stripe remover (MDSR) method based on nonsubsampled contourlet transform (NSCT), which can be used for both unidirectional and multidirectional LSFM. In MDSR, a fast Fourier transform (FFT) filter is designed in the NSCT domain to shrink the stripe components and eliminate the noise. Benefiting from the properties of being multiscale and multidirectional, MDSR succeeds in eliminating stripe artifacts in both unidirectional and multidirectional LSFM. To validate the method, MDSR has been tested on images from a custom-made unidirectional LSFM system and a commercial multidirectional LSFM system, clearly demonstrating that MDSR effectively removes most of the stripe artifacts. Moreover, we performed a comparative experiment with the variational stationary noise remover and the wavelet-FFT methods and quantitatively analyzed the results with a peak signal-to-noise ratio, showing an improved noise removal when using the MDSR method.

Technique for multi-focus image fusion based on fuzzy-adaptive pulse-coupled neural network

Multi-focus image fusion technique can solve the problem that not all the targets in an image are clear in case of imaging in the same scene. In this paper, a novel multi-focus image fusion technique is presented, which is developed by using the nonsubsampled contourlet transform (NSCT) and a proposed fuzzy logic based adaptive pulse-coupled neural network (PCNN) model. In our method, sum-modified Laplacian (SML) is calculated as the motivation for PCNN neurons in NSCT domain. Since the linking strength plays an important role in PCNN, we propose an adaptively fuzzy way to determine it by computing each coefficient’s importance relative to the surrounding coefficients. Combined with human visual perception characteristics, the fuzzy membership value is employed to automatically achieve the degree of importance of each coefficient, which is utilized as the linking strength in PCNN model. Experimental results on simulated and real multi-focus images show that the proposed technique has a superior performance to series of exist fusion methods.

Hybrid NSCT domain multiple watermarking for medical images

Medical image watermarking is a challenging area of research. High bandwidth, secure transmission of patient's data among hospitals and hiding capacity are major concerns in medical image watermarking. Recently, wavelet transforms and their hybrid combinations have been widely used for this purpose. The conventional wavelet transforms suffer from shift sensitivity and have low hiding capacity. Therefore, the performance of hybrid combinations of wavelet transforms for image watermarking is limited. The shortcomings of wavelet transforms can be overcome with the use of Nonsubsampled contourlet transform (NSCT) which is shift invariant in nature and provides rich directional information. For these reasons, in this work we propose NSCT based technique for medical image watermarking which combines discrete cosine transform (DCT) along with Multiresolution Singular value decomposition (MSVD) and Arnold transform in order to increase robustness, capacity and imperceptibility. In the proposed work, multiple (three) image watermarks have been used for a single cover medical image. We have embedded three image watermarks into NSCT coefficients of the cover image. Among which two of them are image watermarks and third is encrypted text watermark. By using NSCT, embedding capacity has been increased and it becomes more resistant to geometrical attacks. Also, hybrid combination of NSCT with DCT, MSVD and Arnold transform increases the perceptual quality and security of watermarked image. Experimental results showed that the proposed method provides high robustness against attacks like JPEG compression, Rotation, Resizing, noise, Blurring and found better than existing hybrid methods for medical image watermarking.

Directive Contrast based Multimodal Image Fusion in UWT and NSCT Domain

Objectives: The purpose of image fusion is to provide information integrated from different images to eliminate redundancy and contradiction between images. This paper presents medical image fusion using pixel level fusion with multi-level wavelet transform to obtain low frequency and high frequency subbands. This involves a pixel level averaging rule for appropriate fusion to integrate the decomposed image coefficients subbands. Methods: A two-stage multimodal fusion framework uses the cascaded combination of Un-decimated Wavelet Transform (UWT) and Non Sub-sampled Contour let Transform (NSCT) domains are used. Findings: This is to improve upon the shift variance, directionality, and phase information in the finally fused image. Applications/Improvements: A mathematical analysis of fused images is done using dedicated fusion metrics. The system performance is evaluated by using the parameters such as Peak signal to noise ratio, correlation and entropy.

Improved Multiscale Edge Detection Method for Polarimetric SAR Images

This letter presents a multiscale edge detection method for multilook polarimetric synthetic aperture radar (PolSAR) images based on the nonsubsampled contourlet transform (NSCT). The NSCT can provide flexible multiscale and directional decomposition. In the multiscale decomposition, the coefficients of the nonsubsampled pyramid in the NSCT are calculated via maximizing the polarimetric contrast between the adjacent subband levels, instead of using the difference of the adjacent subbands as used in the additive noise model. By this way, we make the NSCT applicable to PolSAR data and multiband data. Then, the edges are detected in the NSCT domain based on a fusion of the directional subband coefficients at different scales. Experimental results with both simulated and real PolSAR data show that the present approach is robust to noise and the extracted edges are complete and continuous.

A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain

The intelligibility of an image can be influenced by the pseudo-Gibbs phenomenon, a small dynamic range, low-contrast, blurred edge and noise pollution that occurs in the process of image enhancement. A new remote sensing image enhancement method using mean filter and unsharp masking methods based on non-subsampled contourlet transform (NSCT) in the scope for greyscale images is proposed in this paper. First, the initial image is decomposed into the NSCT domain with a low-frequency sub-band and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. The mean filter is used for the coefficients of the first high-frequency sub-band. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The experimental results show that the proposed method is superior to other methods in improving image definition, image contrast and enhancing image edges.

Image Quality Assessment with Saliency Map in Nonsubsampled Contourlet Transform Domain

Many researchers evaluate images by objective image quality assessments instead of subjective ones. Objective image quality assessment sets up mathematical model according to the human visual system, and it evaluates the image quality through the reference image and the distorted image. The Structural Similarity Index (SSIM) is one of the most classical methods in image quality assessment. However, SSIM has several inherent shortcomings. First, SSIM does not take spatial position, spatial frequency, or direction into account. Second, SSIM considers that different regions in an image have equal importance for overall image quality assessment. Third, it is unreasonable to use fixed parameters for various images. To overcome these shortcomings, we propose a new method of image quality assessment based on Nonsubsampled Contourlet Transform (NSCT). Firstly, NSCT is performed to decompose the image into a low-pass map and high-pass ones. Then, low-pass and high-pass maps are respectively assessed with different strategies. In addition, saliency map is added to describe the importance of different regions in an image. Last, we proposed an approach to calculate the adaptive parameters for various images. Experimental comparisons among five public benchmark databases demonstrate that the proposed method is better than other competing methods.

Multimodal Sensor Medical Image Fusion Based on Type-2 Fuzzy Logic in NSCT Domain

Multimodal medical image fusion plays a vital role in different clinical imaging sensor applications. This paper presents a novel multimodal medical image fusion method that adopts a multiscale geometric analysis of the nonsubsampled contourlet transform (NSCT) with type-2 fuzzy logic techniques. First, the NSCT was performed on preregistered source images to obtain their high- and low-frequency subbands. Next, an effective type-2 fuzzy logic-based fused rule is proposed for fusion of the high-frequency subbands. In the presented fusion approach, the local type-2 fuzzy entropy is introduced to automatically select high-frequency coefficients. However, for the low-frequency subbands, they were fused by a local energy algorithm based on the corresponding image’s local features. Finally, the fused image was constructed by the inverse NSCT with all composite subbands. Both subjective and objective evaluations showed better contrast, accuracy, and versatility in the proposed approach compared with state-of-the-art methods. Besides, an effective color medical image fusion scheme is also given in this paper that can inhibit color distortion to a large extent and produce an improved visual effect.

Watermark Detection in NSCT-Domain

In this paper, we presented a Rao hypothesis detector by modeling Cauchy distribution for the NonSubsampled Contourlet Transform (NSCT) subband coefficients in the field of additive spread spectrum image watermarking. Firstly, the NSCT subband coefficients were modeled following the Cauchy distributions. Secondly, a blind Rao test watermark detector was derived in the NSCT domain. Finally, the robustness of the proposed watermarking scheme was evaluated. Experimental results show that the proposed Rao detector guarantees the lowest probability of miss under the given probability of false alarm.

PolSAR Image Classification via D-KSVD and NSCT-Domain Features Extraction

Polarimetric synthetic aperture radar (PolSAR) image classification is a powerful and important application in remote sensing. In this letter, we propose a PolSAR image classification method based on discriminative dictionary learning (D-KSVD) model and nonsubsampled contourlet transform (NSCT)-domain features. The D-KSVD model is used in our experiment to classify PolSAR images and is more time efficient and accurate when compared with the sparse representation classifier (SRC) used by other researchers to the PolSAR image classification. This is due to the fact that the D-KSVD model employs linear classifier rather than compute the reconstruction error employed by SRC. On constructing an effective dictionary of D-KSVD, we use NSCT to extract polarization features from the PolSAR coherency matrix. However, the low-frequency coefficients of NSCT domain have better discrimination ability than others, and we consider them as the classification features; hence, we adopt them into the D-KSVD model to obtain a dictionary which accommodates a linear classifier. Our experimental results of two real PolSAR images indicate that the proposed PolSAR image classification method based on D-KSVD model and NSCT-domain features extraction approach achieves better results with time efficiency and accuracy.

Medical Image Fusion Algorithm based on Local Average Energy-Motivated PCNN in NSCT Domain

Medical Image Fusion (MIF) can improve the performance of medical diagnosis, treatment planning and image-guided surgery significantly through providing high-quality and rich-information medical images. Traditional MIF techniques suffer from common drawbacks such as: contrast reduction, edge blurring and image degradation. Pulse-coupled Neural Network (PCNN) based MIF techniques outperform the traditional methods in providing high-quality fused images due to its global coupling and pulse synchronization property; however, the selection of significant features that motivate the PCNN is still an open problem and plays a major role in measuring the contribution of each source image into the fused image. In this paper, a medical image fusion algorithm is proposed based on the Non-subsampled Contourlet Transform (NSCT) and the Pulse-Coupled Neural Network (PCNN) to fuse images from different modalities. Local Average Energy is used to motivate the PCNN due to its ability to capture salient features of the image such as edges, contours and textures. The proposed approach produces a high quality fused image with high contrast and improved content in comparison with other image fusion techniques without loss of significant details on both levels: the visual and the quantitative.

NSCT Domain Additive Watermark Detection Using RAO Hypothesis Test and Cauchy Distribution

We presented a RAO hypothesis detector by modeling Cauchy distribution for the Nonsubsampled Contourlet Transform (NSCT) subband coefficients in the field of additive spread spectrum image watermarking. Firstly, the NSCT subband coefficients were modeled following the Cauchy distributions, and the Fit of Goodness shows that Cauchy distribution fits the NSCT subband coefficients more accurately than the Generalized Gaussian Distribution (GGD) commonly used. Secondly, a blind RAO test watermark detector was derived in the NSCT domain, which does not need the knowledge of embedding strength at the receiving end. Finally, compared to the other three state-of-art detectors, the robustness of the proposed watermarking scheme was evaluated when the watermarked images were attacked by JPEG compression, random noise, low pass filtering, and median filtering. Experimental results show that, compared with the other three detectors, the proposed RAO detector guarantees the lower probability of miss under the given probability of false alarm.

Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an important role in clinical applications. In this paper, an image fusion technique for the fusion of multimodal medical images is proposed based on Non-Subsampled Contourlet Transform. The proposed technique uses the Non-Subsampled Contourlet Transform (NSCT) to decompose the images into lowpass and highpass subbands. The lowpass and highpass subbands are fused by using mean based and variance based fusion rules. The reconstructed image is obtained by taking Inverse Non-Subsampled Contourlet Transform (INSCT) on fused subbands. The experimental results on six pairs of medical images are compared in terms of entropy, mean, standard deviation, QAB/F as performance parameters. It reveals that the proposed image fusion technique outperforms the existing image fusion techniques in terms of quantitative and qualitative outcomes of the images. The percentage improvement in entropy is 0% - 40%, mean is 3% - 42%, standard deviation is 1% - 42%, QAB/Fis 0.4% - 48% in proposed method comparing to conventional methods for six pairs of medical images.

Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains

Multimodal medical image fusion is effectuated to minimize the redundancy while augmenting the necessary information from the input images acquired using different medical imaging sensors. The sole aim is to yield a single fused image, which could be more informative for an efficient clinical analysis. This paper presents a two-stage multimodal fusion framework using the cascaded combination of stationary wavelet transform (SWT) and non sub-sampled Contourlet transform (NSCT) domains for images acquired using two distinct medical imaging sensor modalities (i.e., magnetic resonance imaging and computed tomography scan). The major advantage of using a cascaded combination of SWT and NSCT is to improve upon the shift variance, directionality, and phase information in the finally fused image. The first stage employs a principal component analysis algorithm in SWT domain to minimize the redundancy. Maximum fusion rule is then applied in NSCT domain at second stage to enhance the contrast of the diagnostic features. A quantitative analysis of fused images is carried out using dedicated fusion metrics. The fusion responses of the proposed approach are also compared with other state-of-the-art fusion approaches; depicting the superiority of the obtained fusion results.

An Affinity-Based Algorithm in Nonsubsampled Contourlet Transform Domain: Application to Synthetic Aperture Radar Image Denoising

An algorithm in which affinity is merged into nonsubsampled contourlet transform (NSCT) to denoise synthetic aperture radar (SAR) image is proposed. The important information (boundaries or details) or non-important information (homogeneous area) in a SAR image is estimated based on affinity matrix, by which the affinity-based denoising assigns a high affinity element to the coefficients of NSCT that belong to same region. The foreground and background in NSCT domain are automatically initialized which avoids the need for user initialization. Foreground probability obtained by optimizing objective function can be used to achieve the posterior ratio. Combining the posterior ratio and prior ratio, we can obtain the shrunk coefficients. The proposed algorithm was applied to real SAR images denoising and compared through the SAR image vision effect, the equivalent number of looks (ENL) and the edge sustain index (ESI). Experimental results show that the proposed algorithm outperforms the compared algorithms and achieves the better denoising result and edge preservation.

Adaptive directional decomposition in non sub sample contourlet transform domain for single image super resolution

The crucial point of research about single-image super-resolution is to enhance the size of an image without fiddle its quality. To conserve the quality of an enhanced image, a novel learning based approach in Non Sub Sampled Contourlet Transform (NSCT) domain is proposed in this paper. The NSCT constitute multiscale -multidirectional decomposition of an image. An algorithm, proposed for adaptive multidirectional decomposition picks up the optimal benefit of the directionality offered by NSCT. A sharp initialization, obtained via learning in NSCT domain, is able to generate a high resolution image with minimum edge artifacts. Experiments on various images show the effectiveness of the proposed algorithm.

A novel multi-focus image fusion method using PCNN in nonsubsampled contourlet transform domain

A novel multi-focus image fusion method using modified pulse coupled neural network (PCNN) in nonsubsampled contourlet transform (NSCT) domain is presented in this study. Different scales and directions are obtained by image decomposition using the NSCT at first. Then to retain more edges and textures, the edge feature is used to motive the improved PCNN model. The coefficients in the NSCT domain with large firing times are selected as coefficients of the fused image. To testify the performance of our proposed algorithm, simulation experiments are conducted on it. The experimental results showed that the proposed algorithm produces better results compared to other state-of-the-art algorithms neither visual effects nor objective indicators.

A new contrast based multimodal medical image fusion framework

Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. The main motivation is to fuse different multimodal information into a single output. In this instance, we propose a novel framework for spatially registered multimodal medical image fusion, which is primarily based on the non-subsampled contourlet transform (NSCT). The proposed method enables the decomposition of source medical images into low- and high-frequency bands in NSCT domain. Different fusion rules are then applied to the varied frequency bands of the transformed images. Fusion coefficients are achieved by the following fusion rule: low-frequency components are fused using an activity measure based on the normalized Shannon entropy, which essentially selects low-frequency components from the focused regions with high degree of clearness. In contrast, high-frequency components are fused using the directive contrast, which essentially collects all the informative textures from the source. Integrating these fusion rules, more spatial feature and functional information can be preserved and transferred into the fused images. The performance of the proposed framework is illustrated using four groups of human brain and two clinical bone images from different sources as our experimental subjects. The experimental results and comparison with other methods show the superior performance of the framework in both subjective and objective assessment criteria.

A fusion algorithm for infrared and visible images based on adaptive dual-channel unit-linking PCNN in NSCT domain

In this paper, a novel fusion algorithm based on the adaptive dual-channel unit-linking pulse coupled neural network (PCNN) for infrared and visible images fusion in nonsubsampled contourlet transform (NSCT) domain is proposed. The flexible multi-resolution and directional expansion for images of NSCT are associated with global coupling and pulse synchronization characteristic of dual-PCNN. Compared with other dual-PCNN models, the proposed model possesses fewer parameters and is not difficult to implement adaptive, which is more suitable for image fusion. Firstly, the source images were multi-scale and multi-directional decomposed by NSCT. Then, to make dual-channel PCNN adaptive, the average gradient of each pixel was presented as the linking strength, and the time matrix was presented to determine the iteration number adaptively. In this fusion scheme, a novel sum modified-Laplacian of low-frequency subband and a modified spatial frequency of high-frequency subband were input to motivate the adaptive dual-channel unit-linking PCNN, respectively. Experimental results demonstrate that the proposed algorithm can significantly improve image fusion performance, accomplish notable target information and high contrast, simultaneously preserve rich details information, and excel other typical current methods in both objective evaluation criteria and visual effect.

A Robust Image Denoising Technique in the Contourlet Transform Domain

The contourlet transform has the benefit of efficiently capturing the oriented geometrical structures of images. In this paper, by incorporating the ideas of Stein’s Unbiased Risk Estimator (SURE) approach in Nonsubsampled Contourlet Transform (NSCT) domain, a new image denoising technique is devised. We utilize the characteristics of NSCT coefficients in high and low subbands and apply SURE shrinkage and bilateral filter respectively. Moreover, SURE-LET strategy is modified to minimize the estimation of the Mean Square Error (MSE) between the clean image and the denoised one in the NSCT domain. The simulation testing has been carried on under the different noise level, and the denoising effect has been evaluated by using the Peak Signal to Noise Ratio (PSNR). The obtained results for different kinds of sample image show that the proposed method in this paper can preserve most important information of images, remove Gaussian white noise more effectively, and get a higher PSNR value, which also has a better visual effect.