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Showing papers on "Contourlet published in 2020"


Journal ArticleDOI
TL;DR: The phase truncation and the bitwise XOR operation, as nonlinear processes, improve the robustness of the presented multi-image encryption scheme against chosen-plaintext attack.

112 citations


Journal ArticleDOI
TL;DR: Experimental evaluation shows that using combination of NSCT, RDWT, SVD and chaotic encryption makes the approach robust, imperceptible, secure and suitable for medical applications.
Abstract: In this paper, a chaotic based secure medical image watermarking approach is proposed. The method is using non sub-sampled contourlet transform (NSCT), redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) to provide significant improvement in imperceptibility and robustness. Further, security of the approach is ensured by applying 2-D logistic map based chaotic encryption on watermarked medical image. In our approach, the cover image is initially divided into sub-images and NSCT is applied on the sub-image having maximum entropy. Subsequently, RDWT is applied to NSCT image and the singular vector of the RDWT coefficient is calculated. Similar procedure is followed for both watermark images. The singular value of both watermarks is embedded into the singular matrix of the cover. Experimental evaluation shows when the approach is subjected to attacks, using combination of NSCT, RDWT, SVD and chaotic encryption it makes the approach robust, imperceptible, secure and suitable for medical applications.

76 citations


Journal ArticleDOI
TL;DR: Modified Histograms Equalization on Fuzzy based Improved Particle Swarm Optimization (FIPSO) is proposed for Dynamic Histogram Equalization which resolves this problem through image contrast enhancement and demonstrates that the current equalization technique attains highest performance against existing techniques in terms of brightness and contrast.

52 citations


Journal ArticleDOI
Lingling Li1, Liyuan Ma1, Licheng Jiao1, Fang Liu1, Qigong Sun1, Jin Zhao1 
TL;DR: Experiments on different spatial resolutions and land coverings of Flevoland, San Francisco Bay, and Germany PolSAR images show that less training data is required and the performance of the proposed explainable deep learning method is comparable to that of the existing state-of-the-art methods.

43 citations


Journal ArticleDOI
TL;DR: A transform domain multi-focus image fusion method based on a novel parameter adaptive DCPCNN (PA-DCPCNN) model, in which the parameters are adaptively estimated using the inputs, is proposed.

42 citations


Journal ArticleDOI
01 Jan 2020
TL;DR: The simulation results show that the proposed watermarking algorithm is robust against most image processing attacks like salt & pepper, cropping, low-pass filter, wiener filter, blurring, etc.
Abstract: This paper proposes a blind and robust color image watermarking method based on a new three-dimensional Henon chaotic map and uses integer wavelet transform, discrete wavelet transform and contourlet transform in embedding and extracting processes. In the presented approach, color images are divided into $$4\times 4$$ main nonoverlapping parts, and one of the transforms is applied to these parts. Then the low–low sub-band of transform is selected. The suggested map is used to find $$2\times 2$$ blocks in the embedding process. The bits of watermark are embedded in the parts of images to increase the robustness of the proposed watermarking scheme. To improve the quality of the final watermark, the suggested technique uses a correction process in the extracting process. In this paper, the bifurcation diagram, Lyapunov exponent, cobweb plot and trajectory diagram are used to show the chaotic behavior of the proposed map. Based on DIEHARD, ENT and NIST test suites, the suggested map can be used as a pseudo-random number generator. The simulation results show that the proposed watermarking algorithm is robust against most image processing attacks like salt & pepper, cropping, low-pass filter, wiener filter, blurring, etc. The comparison results between the suggested watermarking scheme, and some similar methods show that the presented technique has good performance, imperceptibility, acceptable robustness and outperforms most related methods.

40 citations


Journal ArticleDOI
TL;DR: A novel fusion method that combines separable dictionary optimization with Gabor filter in non-subsampled contourlet transform (NSCT) domain is proposed, leading to the state-of-art performance on both visual quality and objective assessment.
Abstract: Sparse representation (SR) has been widely used in image fusion in recent years. However, source image, segmented into vectors, reduces correlation and structural information of texture with conventional SR methods, and extracting texture with the sliding window technology is more likely to cause spatial inconsistency in flat regions of multi-modality medical fusion image. To solve these problems, a novel fusion method that combines separable dictionary optimization with Gabor filter in non-subsampled contourlet transform (NSCT) domain is proposed. Firstly, source images are decomposed into high frequency (HF) and low frequency (LF) components by NSCT. Then the HF components are reconstructed sparsely by separable dictionaries with iterative updating sparse coding and dictionary training. In the process, sparse coefficients and separable dictionaries are updated by orthogonal matching pursuit (OMP) and manifold-based conjugate gradient method, respectively. Meanwhile, the Gabor energy as weighting factor is utilized to guide the LF components fusion, and this further improves the fusion degree of low-significant feature in the flat regions. Finally, the fusion components are transformed to obtain fusion image by inverse NSCT. Experimental results demonstrate the more competitive results of the proposal, leading to the state-of-art performance on both visual quality and objective assessment.

36 citations


Journal ArticleDOI
29 Sep 2020
TL;DR: The experimental works show that multiresolution approaches produced better performance than the deep learning approaches, especially, Shearlet transform outperformed at all.
Abstract: COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19. And new methods have been searching for determination of the COVID-19. In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out. Chest X-ray images are used as input to the proposed approach. As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective. To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images. Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications. The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study. A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works. The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision. As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out. To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered. For deep feature extraction, pretrained, ResNet50 model is employed. For classification of the deep features, the Support Vector Machines (SVM) classifier is used. The ResNet50 model is also used in the fine-tuning. The experimental works show that multiresolution approaches produced better performance than the deep learning approaches. Especially, Shearlet transform outperformed at all. 99.29% accuracy score is obtained by using Shearlet transform.

36 citations


Journal ArticleDOI
TL;DR: This method is presented a blind and robust watermarking method to copyright protection in digital video using the singular value decomposition (SVD) and pseudo-random numbers generated by the proposed new chaotic map, which is a generalized two-dimensional complex map based on the Newton model.
Abstract: The rapid growth of fast communication networks for digital video transmission has created a need to copyright protection for these media. Digital video can be manipulated easily by users with various motivations. Compression is the most common attack that users can apply on videos in order to eliminate digital video copyright. Proposed technique in this article is specially designed for resisting against compression attacks. This method is presented a blind and robust watermarking method to copyright protection in digital video. In the proposed method, the coefficients of the contourlet transform are extracted and then encrypted watermark embedded into video with using the singular value decomposition (SVD). Embedding watermark in SVD domain increases the robustness of proposed method against attacks. In the embedding process and watermark encryption, pseudo-random numbers generated by the proposed new chaotic map, which is a generalized two-dimensional complex map based on the Newton model. The PSNR, SSIM, BER, and NCC measures examine the performance of the proposed method in terms of robustness and visual quality.

34 citations


Journal ArticleDOI
01 Jan 2020-Optik
TL;DR: A novel and effective image enhanced fusion via a hybrid decomposition of non-subsample contourlet transform and morphological sequential toggle operator (MSTO) is proposed, which can largely improve the contrast and visible effect of final fusion image.

32 citations


Journal ArticleDOI
21 Jan 2020
TL;DR: The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases.
Abstract: Stroke is one of the prominent causes of death in the recent days. The existence of susceptible plaque in the carotid artery can be used in ascertaining the possibilities of cardiovascular diseases and long-term disabilities. The imaging modality used for early screening of the disease is B-mode ultrasound image of the person in the artery area. The objective of this article is to give a widespread review of the imaging modes and methods used for studying the carotid artery for identifying stroke, atherosclerosis and related cardiovascular diseases. We encompass the review in methods used for artery wall tracking, intima-media, and lumen segmentation which will help in finding the extent of the disease. Due to the characteristics of the imaging modality used, the images have speckle noise which worsens the image quality. Adaptive homomorphic filtering with wavelet and contourlet transforms, Levy Shrink, gamma distribution were used for image denoising. Learning-based neural network approaches for denoising give better edge preservation. Domain knowledge-based segmentation approaches have proved to provide more accurate intima-media thickness measurements. There is a requirement of useful fully automatic segmentation approaches, 3D, 4D systems, and plaque motion analysis. Taking into consideration the image priors like geometry, imaging physics, intensity and temporal data, image analysis has to be performed. Encouragingly more research has focused on content-specific segmentation and classification techniques. With the evaluation of machine learning algorithms, classifying the image as with or without a fat deposit has gained better accuracy and sensitivity. Machine learning-based approaches like self-organizing map, k-nearest neighborhood and support vector machine achieve promising accuracy and sensitivity in classification. The literature reveals that there is more scope in identifying a patient-specific model in a fully automatic manner.

Journal ArticleDOI
TL;DR: A proper diagnosis method of polyp is proposed using a fusion of contourlet transform and fine-tuned VGG19 pre-trained model from enhanced endoscopic 224 × 224 patch images to diagnose polyps during real-time endoscopy.

Journal ArticleDOI
TL;DR: A new infrared and visible image fusion method employing non-subsampled contourlet transform (NSCT) with intuitionistic fuzzy sets with outperforms the advanced fusion methods in terms of objective assessment and visual quality.

Journal ArticleDOI
TL;DR: The approaches of the embedding and the de-embedding in case of learning algorithm of the aforementioned neural network through individual training data set are considered in the present research to carry out a series of experiments with different scenarios for the purpose of verifying the effectiveness of the proposed approach.
Abstract: In the research presented here, the general idea of watermarking framework is analyzed to deal with color image under a set of attacks through a neural network-based approach. It is realized in the area of transformation, especially with a focus on contourlet transform to address the proposed technique, as long as the bands of the suitable coefficients are accurately chosen. In summary, there is the logo information that is embedded in the edge of color image, while the Zenzo edge detector is correspondingly realized to handle the approach. In fact, the edge of the second subband is acquired, and subsequently, the capability of the above-referenced edge is calculated. A number of techniques are discussed to cope with the above-captioned watermarking framework through the new integration of contourlet transform in association with the multilayer perceptron to extract the logo information, appropriately. The approaches of the embedding and the de-embedding in case of learning algorithm of the aforementioned neural network through individual training data set are considered in the present research to carry out a series of experiments with different scenarios for the purpose of verifying the effectiveness of the proposed approach, obviously.

Journal ArticleDOI
TL;DR: Experimental results demonstrate that the proposed watermarking scheme preforms better in terms of invisibility and robustness than other related schemes.
Abstract: A blind watermarking algorithm in multiple transform domains is presented for copyright protection. This robust algorithm is designed by fusing contourlet transform (CT), discrete cosine transform (DCT) and singular value decomposition (SVD). The host image is first decomposed by one-level CT and its low frequency sub-band is partitioned into 8 × 8 non-overlapping blocks. Then, each block is transformed by DCT and several middle frequency DCT coefficients with good stability are selected to construct the carrier matrix. Finally, the watermark is embedded by modifying the largest singular values of two carrier matrices. Besides, the geometric distortion factor is estimated with the speed up robust features (SURF) algorithm. The proposed watermarking scheme is evaluated in terms of imperceptibility and robustness. Experimental results demonstrate that the proposed watermarking scheme preforms better in terms of invisibility and robustness than other related schemes.

Journal ArticleDOI
TL;DR: A multiscale geometric analysis (MGA) convolutional neural network (CNN) is proposed, which can achieve good results even under a low SNR and can effectively suppress the low-frequency noise, and the effective signal almost has no energy loss.
Abstract: Existing denoising algorithms often need to meet some premise assumptions and applicable conditions, such as the signal-to-noise ratio (SNR) cannot be too low, and the noise needs to obey a specific distribution (such as Gaussian distribution) and to satisfy some properties (such as stationarity). For the desert noise that shares the same frequency band with the effective signal and has complex characteristics (nonlinear, nonstationary, and non-Gaussian), it is difficult to find a universally applicable method. In response to this problem, a multiscale geometric analysis (MGA) convolutional neural network (CNN) is proposed in this article. One of the most important features of the CNN is that it can extract data-rich intrinsic information from the training set without relying on a priori assumption. By introducing the CNN into the MGA, a new kind of denoising method can be created, which can achieve good results even under a low SNR. This article takes the nonsubsampled contourlet transform as an example to create a denoising network named NC-CNN for high-efficiency and intelligent denoising of desert seismic data. The processing results of synthetic seismic records and field seismic records prove that NC-CNN can effectively suppress the low-frequency noise (random noise and surface wave), and the effective signal almost has no energy loss. In addition, the reconstruction ability of the missing signals is also an advantage of this method.

Journal ArticleDOI
TL;DR: In this article, a segmentation network combined with nonsubsampled contourlet transform (NSCT) is proposed to extract marine raft aquaculture (MFA) areas using Sentinel-1 images.
Abstract: Marine raft aquaculture (MFA) plays an important role in the marine economy and ecosystem. With the characteristics of covering a large area and being sparsely distributed in sea area, MFA monitoring suffers from the low efficiency of field survey and poor data of optical satellite imagery. Synthetic aperture radar (SAR) satellite imagery is currently considered to be an effective data source, while the state-of-the-art methods require manual parameter tuning under the guidance of professional experience. To preclude the limitation, this paper proposes a segmentation network combined with nonsubsampled contourlet transform (NSCT) to extract MFA areas using Sentinel-1 images. The proposed method is highlighted by several improvements based on the feature analysis of MFA. First, the NSCT was applied to enhance the contour and orientation features. Second, multiscale and asymmetric convolutions were introduced to fit the multisize and strip-like features more effectively. Third, both channel and spatial attention modules were adopted in the network architecture to overcome the problems of boundary fuzziness and area incompleteness. Experiments showed that the method can effectively extract marine raft culture areas. Although further research is needed to overcome the problem of interference caused by excessive waves, this paper provides a promising approach for periodical monitoring MFA in a large area with high efficiency and acceptable accuracy.

Journal ArticleDOI
TL;DR: A novel single image super-resolution method based on progressive-iterative approximation that significantly outperforms the state-of-the-art methods in terms of both subjective and objective measures is proposed.
Abstract: In this paper, a novel single image super-resolution (SR) method based on progressive-iterative approximation is proposed. To preserve textures and clear edges, the image SR reconstruction is treated as an image progressive-iterative fitting procedure and achieved by iterative interpolation. Due to different features in different regions, we first employ the nonsubsampled contourlet transform (NSCT) to divide the image into smooth regions, texture regions, and edges. Then, a hybrid interpolation scheme based on curves and surfaces is proposed, which differs from the traditional surface interpolation methods. Specifically, smooth regions are interpolated by the non-uniform rational basis spline (NURBS) surface geometric iteration. To retain textures, control points are increased, and the progressive-iterative approximation of the NURBS surface is employed to interpolate the texture regions. By considering edges in an image as curve segments that are connected by pixels with dramatic changes, we use NURBS curve progressive-iterative approximation to interpolate the edges, which sharpens the edges and can maintain the image edge structure without jaggy and block artifacts. The experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of both subjective and objective measures.

Book ChapterDOI
01 Jan 2020
TL;DR: A novel approach called the HARIS-Heuristic approach for real-time segmentation to analyze a brain's magnetic resonance image (MRI) for tumor identification, which outperforms its counterparts in accurately identifying tumors with the least computational time.
Abstract: In this chapter, we propose a novel approach called the HARIS-Heuristic approach for real-time segmentation to analyze a brain's magnetic resonance image (MRI) for tumor identification. MRIs are pre-processed using an adaptive contourlet transform to remove noise and a structural augmentation technique to remove the skull region. Resultant MRIs are fed as an input to HARIS, which segments them based on texture using multiobjective functions. To evaluate the efficiency of HARIS, a comparison is made with existing techniques such as Twin Centric GA-SGO, CNN, and GA-TLBO. HARIS outperforms its counterparts in accurately identifying tumors with the least computational time.

Journal ArticleDOI
TL;DR: The qualitative results show that EMI can greatly improve the defect detection ability of ET and EL, and the image-fusion results of sparse representation (SR) algorithm are superior to the other algorithms.
Abstract: In the process of research, development, production, service, and maintenance of silicon photovoltaic (Si-PV) cells and the requirements for detection technology are becoming more and more important. This paper aims to investigate electromagnetic induction (EMI) and image fusion to improve the detection effect of electrothermography (ET) and electroluminescence (EL) of multidefects in Si-PV cells. First, the principles of ET, EL, and other physical processes including EMI, thermal radiation, and luminescence radiation are analyzed in this paper. ET and EL techniques after EMI improvement are used to detect different defects including scratch, broken gridline, surface impurity, hidden crack, and so on. The qualitative results show that EMI can greatly improve the defect detection ability of ET and EL. Then, an image-fusion rule based on L1 norm is proposed to fuse the sparse vector of the ET and EL images. The integration and complementarity of the two wavelength detection data are achieved. Finally, the image-fusion results of sparse representation (SR) algorithm is compared with discrete wavelet transform, curvelet transform, dual-tree complex wavelet transforms, and nonsubsampled contourlet transform. Five objective evaluation indexes including root mean square error, peak signal-to-noise ratio, correlation coefficient, mutual information, and structural similarity index are used to evaluate the fusion results. Overall evaluation results show that the SR algorithm is superior to the other algorithms.

Journal ArticleDOI
TL;DR: A joint encryption-then-compression-based watermarking scheme for copyright protection and content verification in variety of applications and performance outcomes confirm better robustness and security of the method while preserving the image quality.
Abstract: This article discusses a joint encryption-then-compression-based watermarking scheme for copyright protection and content verification in variety of applications. In this scheme, redundant discrete wavelet transform is first performed to decompose the nonsubsampled contourlet transform host image, and then, the singular value decomposition is applied on transformed coefficients of the host image. Furthermore, same procedure is applied for both watermarks (image and PAN No.). The PAN number is encoded by the quick response (QR) code before hiding into the host image. Finally, 2D hyperchaotic encryption is used to encrypt the watermarked image to make our method more secure against attacks. After this, the encrypted image is then compressed via Huffman compression to reduce the size of the image at an acceptable quality of the reconstructed image. Furthermore, performance outcomes confirm better robustness and security of the method while preserving the image quality.

Journal ArticleDOI
TL;DR: A novel framelet transform based image steganography scheme that hides a secret image into cover image and stego images possess better visual quality and are robust to several popular image processing operations.
Abstract: Steganography and steganalysis are the prominent research fields in information hiding paradigm. This work presents a novel framelet transform based image steganography scheme that hides a secret image into cover image. Perfect reconstruction, sparsity, and stability enables framelet transform to be considered as suitable decomposition technique to obtain transform coefficients. The scheme also benefits from bidiagonal singular value decomposition. Secret information is embedded in singular values of framelet coefficients and stego is obtained. A variety of experiments is conducted to judge the efficacy of proposed method. Simulation results prove that stego images possess better visual quality and are robust to several popular image processing operations. Security performance of proposed method is investigated using various steganalysis schemes that include Gabor filter based, wavelet based and contourlet based steganalysis. Detection accuracy is found to be poor in all cases and confirms the undetectability.

Journal ArticleDOI
TL;DR: The proposed method prevents the medical image from the various attacks such as rotate, crop, histogram, salt & pepper, blur and resize provides the robustness, thereby reduces to 8.19%, 10.88%, 24.27%, 13.21% and 14.35%.
Abstract: This paper implements a novel approach for image steganography based on Hidden Markov Tree (HMT) Contourlet transform. In this paper, the biomedical image considers as a cover image and it is mapped to a specific frequency domain by applying HMT Contourlet transform. Then canny edge detection method implemented to detect the smooth edges to hide the secret data. The secret data is encrypted by using Paillier cryptosystem in a new location of the cover image. Particle Swarm Optimization (PSO) algorithm developed for the selection of the best place to locate the number of particles in a new location. The proposed method prevents the medical image from the various attacks such as rotate, crop, histogram, salt & pepper, blur and resize provides the robustness, thereby reduces to 8.19%, 10.88%, 24.03%, 15.27%, 13.21% and 14.35%. The performance measures of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) are calculated to show the better performance compared with the existing techniques.

Book ChapterDOI
01 Jan 2020
TL;DR: A hybrid combination of the wavelet transforms have been discussed for single and multiple image watermarking, which will take advantage of the characteristics and NSCT, DCT and MSVD to build a robust water marking system in wavelet domain against signal processing and geometrical attacks.
Abstract: Multimedia security has become challenging due to large amount of content generation and its distribution over network. Copyright protection and content authentication are the major key factor that avoids illegal distribution of digital data. However, due to availability of high bandwidth network, copyright violation is very common and many copies of data can be illegally distributed over network. Thus, to ensure multimedia security, image watermarking methods have been introduced which is a kind of information hiding technique. Watermarking provides an effective way to ensure copyright protection and content authentication and can be implemented in spatial and transform domain. The use of wavelet transforms in watermarking increases the embedding capacity and enhances the imperceptibility of the watermarked image. Being motivated from the use of wavelet transforms in image watermarking, in this chapter, a hybrid combination of the wavelet transforms have been discussed for single and multiple image watermarking. The transforms, combined in this chapter, are nonsubsampled contourlet transform (NSCT), discrete cosine transform (DCT) and multiresolution singular value decomposition (MSVD). This hybrid combination will take advantage of the characteristics and NSCT, DCT and MSVD to build a robust watermarking system in wavelet domain against signal processing and geometrical attacks.

Journal ArticleDOI
TL;DR: Experimental results demonstrate that the proposed watermarking scheme performs better in terms of invisibility, robustness and payload than other similar schemes.
Abstract: An invisibility and blind watermarking algorithm based on Schur decomposition and non-subsampled contourlet transform is designed to protect copyright. The cover image is decomposed by the non-subsampled contourlet transform. And the low pass sub-band of the non-subsampled contourlet transform is divided into 8 × 8 non-overlapping blocks. Then each block is performed by the Schur decomposition and the watermark information is embedded by modifying the largest energy element in the Schur domain. Before embedding into the cover image, the original watermark is scrambled with logistic map and Arnold transform to ensure the security. Besides, a synchronization mechanism based on scale invariant feature transform is designed for resisting geometrical attacks. The proposed watermarking algorithm is evaluated with structural similarity index, peak signal to noise ratio and bit error rate. Experimental results demonstrate that the proposed watermarking scheme performs better in terms of invisibility, robustness and payload than other similar schemes.

Journal ArticleDOI
TL;DR: The visual and quantitative outcomes verify that suggested technique outperforms the state-of-the-art fusion techniques.
Abstract: In this work, multiscale decomposition and sparse representation-based multimodal medical image fusion technique is proposed. An efficient denoising technique, feature-preserving regularized Savitzky–Golay filter is applied to obtain noise-free images. The filtered medical images are split into low- and high-pass subbands by non-subsampled shearlet transform (NSST). The sparse coefficient vectors of low-pass subbands are obtained from a pre-learned dictionary, and “max-L1” rule is applied to obtain the fused low-pass subband. However, high-pass subbands are fused using “max-absolute” rule. Lastly, NSST reconstruction is applied to generate the fused multimodal medical image. The non-subsampled contourlet transform, NSST-based fusion using parameter adaptive pulse coupled neural network and phase congruency techniques are also realized for comparative analysis. Multiple experiments on clean and noisy sets are performed for gray and color medical images. The fusion techniques are also tested on infrared–visible image pairs. The visual and quantitative outcomes verify that suggested technique outperforms the state-of-the-art fusion techniques.

Journal ArticleDOI
TL;DR: A texture based change detection method that applies curvelet and contourlet transforms on polarimetric synthetic aperture radar (SAR) images with high change detection accuracy with better edge continuity and greater AUC is proposed.

Journal ArticleDOI
TL;DR: An efficient image classification method based on the nonsubsampled contourlet transform of RGB-channel images and the convolutional neural network and concatenated to exaggerate the discriminative parts of the primary features should improve the corresponding CNN-based image classification methods.

Journal ArticleDOI
TL;DR: A new texture-based conditional random field (CRF) for Synthetic Aperture Radar (SAR) image segmentation is proposed which uses the nonsubsampled contourlet transform (NSCT) as an overcomplete transform which compensates the shortcomings of the traditional contourlets.

Journal ArticleDOI
TL;DR: The authors develop a novel image fusion algorithm for preserving the invariant knowledge of the multimodal image based on non-subsampled contourlet transform (NSCT), which introduces Quadtree decomposition and Bezier interpolation to extract crucial infrared features.
Abstract: Image fusion aims at aggregating the redundant and complementary information in multiple original images, the most challenging aspect is to design robust features and discriminant model, which enhances saliency information in the fused image. To address this issue, the authors develop a novel image fusion algorithm for preserving the invariant knowledge of the multimodal image. Specifically, they formulate a novel unified architecture based on non-subsampled contourlet transform (NSCT). Their method introduces Quadtree decomposition and Bezier interpolation to extract crucial infrared features. Furthermore, they propose a saliency advertising phase congruency-based rule and local Laplacian energy-based rule for low- and high-pass sub-bands fusion, respectively. In this approach, the fusion image could not only combine the local and global features of the source image to avoid smoothing the edge of the target, but also retain the minor scales details and resists the interference noise of the multi-modal image. Both objective assessments and subjective visions of experimental results indicate that the proposed algorithm performs competitively in both objective evaluation criteria and visual quality.