Nonsubsampled Contourlet Transform Research Articles

Bivariate BMM-based hybrid domain image watermark detector

Robustness, imperceptibility, and watermark capacity are three indispensable and contradictory properties for any image watermarking systems. It is a challenging work to achieve the balance among the three important properties. In this paper, by using the bivariate Beta mixture model (Bivariate BMM), we present a statistical image watermark scheme in nonsubsampled Contourlet transform (NSCT)-fractional order Jacobi Fourier moments (FJFMs) amplitude hybrid domain. The whole watermarking algorithm includes two parts: watermark embedding and detection. NSCT is firstly performed on host image to obtain the frequency subbands, and the NSCT subbands are divided into non overlapping blocks. Then, the significant NSCT domain blocks are selected using local binary patterns (LBP). Meanwhile, for each selected NSCT coefficient block, FJFMs are calculated to obtain the NSCT-FJFMs amplitude. Finally, watermark signals are inserted into the amplitude hybrid domain of NSCT-FJFMs. In order to detect accurately watermark signal, the statistical characteristics of NSCT-FJFMs magnitudes are analyzed in detail. Then, NSCT-FJFMs magnitudes are described statistically by Bivariate BMM, which can simultaneously capture the marginal distribution and strong dependencies of NSCT-FJFMs magnitudes. Also, Bivariate BMM parameters are estimated accurately by the rough-enhanced-Bayes mixture estimation & expectation–maximization (REBMIX&EM) approach. Finally, a statistical watermark detector based on the locally optimum (LO) decision rule and Bivariate BMM is developed in NSCT-FJFMs magnitude hybrid domain. Also, the closed-form expressions on the LO statistic is derived and the receiver operating characteristic (ROC) about our detector is analyzed. Extensive experimental results show the superiority of the proposed image watermark detector over some state-of-the-art statistical watermarking methods.

Complex-valued image reconstruction for compressed sensing MRI using hierarchical constraint

In Magnetic Resonance Imaging (MRI), the sequential acquisition of raw complex-valued image data in Fourier space, also known as k-space, results in extended examination times. To speed up the MRI scans, k-space data are usually undersampled and processed using numerical techniques such as compressed sensing (CS). While the majority of CS-MRI algorithms primarily focus on magnitude images due to their significant diagnostic value, the phase components of complex-valued MRI images also hold substantial importance for clinical diagnosis, including neurodegenerative diseases. In this work, complex-valued MRI reconstruction is studied with a focus on the simultaneous reconstruction of both magnitude and phase images. The proposed algorithm is based on the nonsubsampled contourlet transform (NSCT) technique, which offers shift invariance in images. Instead of directly transforming the complex-valued image into the NSCT domain, we introduce a wavelet transform within the NSCT domain, reducing the size of the sparsity of coefficients. This two-level hierarchical constraint (HC) enforces sparse representation of complex-valued images for CS-MRI implementation. The proposed HC is seamlessly integrated into a proximal algorithm simultaneously. Additionally, to effectively minimize the artifacts caused by sub-sampling, thresholds related to different sub-bands in the HC are applied through an alternating optimization process. Experimental results show that the novel method outperforms existing CS-MRI techniques in phase-regularized complex-valued image reconstructions.

An NSCT-Based Multifrequency GPR Data-Fusion Method for Concealed Damage Detection

Ground-penetrating radar (GPR) is widely employed as a non-destructive tool for subsurface detection of transport infrastructures. Typically, data collected by high-frequency antennas offer high resolution but limited penetration depth, whereas data from low-frequency antennas provide deeper penetration but lower resolution. To simultaneously achieve high resolution and deep penetration via a composite radargram, a Non-Subsampled Contourlet Transform (NSCT) algorithm-based multifrequency GPR data-fusion method is proposed by integrating NSCT with appropriate fusion rules, respectively, for high-frequency and low-frequency coefficients of decomposed radargrams and by incorporating quantitative assessment metrics. Despite the advantages of NSCT in image processing, its applications to GPR data fusion for concealed damage identification of transport infrastructures are rarely reported. Numerical simulation, tunnel model test, and on-site road test are conducted for performance validation. The comparison between the evaluation metrics before and after fusion demonstrates the effectiveness of the proposed fusion method. Both shallow and deep hollow targets hidden in the simulated concrete structure, real tunnel model, and road are identified through one radargram obtained by fusing different radargrams. The significance of this study is producing a high-quality composite radargram to enable multi-depth concealed damage detection and exempting human interference in the interpretation of multiple radargrams.

Underwater Bubble Plume Recognition Algorithm Based on Multi-Feature Fusion Understanding

Underwater bubble plume images contain a wealth of information on wave field and flow characteristics, which can provide valuable research data for marine development, environmental protection, and underwater surveys. However, based on fusing image features and wave field environment features, identifying accurately the underwater bubble plume is still very difficult. In order to improve the accuracy and robustness of bubble plume identification in complex underwater environments, an underwater bubble plume recognition algorithm based on multi-feature fusion understanding is proposed. In this paper, a weight-independent dual-channel residual convolutional neural network (CNN) for feature extraction of the original optical images and the nonsubsampled contourlet transform (NSCT) low-frequency images, and the multi-scale composite feature map groups are generated. Then adaptive fusion is performed based on the feature contribution of the target in different types of images. Next, logical region of interest (ROI) masks are generated by the attention mechanism and superimposed on the fused image to further highlight the target features. Finally, the multi-scale dual-channel fused feature maps containing ROI masks are used for underwater bubble plume target recognition. The experimental results show that the designed recognition network can effectively fuse the features of the original optical images and the NSCT low-frequency imagers, improve the depth of information fusion, and retain the target texture features and the morphological features while reducing the interference of the background information, and have good recognition accuracy and robustness for multi-scale bubble targets in the underwater environment.

An Adaptive Migration Collaborative Network for Multimodal Image Classification.

The multispectral (MS) and the panchromatic (PAN) images belong to different modalities with specific advantageous properties. Therefore, there is a large representation gap between them. Moreover, the features extracted independently by the two branches belong to different feature spaces, which is not conducive to the subsequent collaborative classification. At the same time, different layers also have different representation capabilities for objects with large size differences. In order to dynamically and adaptively transfer the dominant attributes, reduce the gap between them, find the best shared layer representation, and fuse the features of different representation capabilities, this article proposes an adaptive migration collaborative network (AMC-Net) for multimodal remote-sensing (RS) images classification. First, for the input of the network, we combine principal component analysis (PCA) and nonsubsampled contourlet transformation (NSCT) to migrate the advantageous attributes of the PAN and the MS images to each other. This not only improves the quality of images themselves, but also increases the similarity between the two images, thereby reducing the representational gap between them and the pressure on the subsequent classification network. Second, for the interaction on the feature migrate branch, we design a feature progressive migration fusion unit (FPMF-Unit) based on the adaptive cross-stitch unit of correlation coefficient analysis (CCA), which can make the network automatically learn the features that need to be shared and migrated, aiming to find the best shared-layer representation for multifeature learning. And we design an adaptive layer fusion mechanism module (ALFM-Module), which can adaptively fuse features of different layers, aiming to clearly model the dependencies among multiple layers for different sized objects. Finally, for the output of the network, we add the calculation of the correlation coefficient to the loss function, which can make the network converge to the global optimum as much as possible. The experimental results indicate that AMC-Net can achieve competitive performance. And the code for the network framework is available at: https://github.com/ru-willow/A-AFM-ResNet.

Multimodal Medical Image Fusion based on the VGG19 Model in the NSCT Domain

Aim: To deal with the drawbacks of the traditional medical image fusion methods, such as the low preservation ability of the details, the loss of edge information, and the image distortion, as well as the huge need for the training data for deep learning, a new multi-modal medical image fusion method based on the VGG19 model and the non-subsampled contourlet transform (NSCT) is proposed, whose overall objective is to simultaneously make the full use of the advantages of the NSCT and the VGG19 model. Methodology: Firstly, the source images are decomposed into the high-pass and low-pass subbands by NSCT, respectively. Then, the weighted average fusion rule is implemented to produce the fused low-pass sub-band coefficients, while an extractor based on the pre-trained VGG19 model is constructed to obtain the fused high-pass subband coefficients. Result and Discussion: Finally, the fusion results are reconstructed by the inversion transform of the NSCT on the fused coefficients. To prove the effectiveness and the accuracy, experiments on three types of medical datasets are implemented. Conclusion: By comparing seven famous fusion methods, both of the subjective and objective evaluations demonstrate that the proposed method can effectively avoid the loss of detailed feature information, capture more medical information from the source images, and integrate them into the fused images.

Ring artifacts suppression for X-ray CT images by fusion of dual-domain images based on improved UNet

The ring artifact is a typical artifact in X-ray Computed Tomography (CT) images, the existence of which can reduce the image's quality, change the structure and details and affects the interpretation of image information. This paper conducts research from two aspects: tomographic images and sinograms, and designs a dual-domain artifact suppression network. By introducing the Inception module and the residual module, the network's ability to extract features is improved, and the gradient diffusion is suppressed. By customizing the loss function, the network's recognition of ring artifacts is improved and the network's convergence is accelerated. Finally, the outputs of the dual-domain are fused based on the Nonsubsampled Contourlet Transform (NSCT) fusion theory to improve the quality of the image. In addition, three experiments of CT scanning are designed based on microfocus cone-beam CT system to verify the effectiveness. The model is trained and tested using real datasets. Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Squared Error (MSE) are employed as evaluation indicators of image quality. Finally, the proposed method is compared with typical algorithms for artifact suppression. The experimental results show that the method effectively protects the structure information in CT images while suppressing ring artifacts to the greatest extent. The PSNR, SSIM, and MSE of the processed images can respectively reach 36.167 dB, 0.9847 and 6.87 × e-4.

Comparative evaluation of volumetry estimation from plain and contrast enhanced computed tomography liver images.

Surgery planning for liver tumour is carried out using contrast enhanced computed tomography (CECT) images to determine the optimal resection strategy and to assess the volume of liver and tumour. Current surgery planning tools interpret even the functioning liver cells present within the tumour boundary as tumour. Plain CT images provide inadequate information for treatment planning. This work attempts to address two shortcomings of existing surgery planning tools: (i) to delineate functioning liver cells from the non-functioning tumourous tissues within the tumour boundary and (ii) to provide 3D visualization and actual tumour volume from the plain CT images. All slices of plain CT images containing liver are enhanced by means of fuzzy histogram equalization in Non-Subsampled Contourlet Transform (NSCT) domain prior to 3D reconstruction to clearly delineate liver, non-functioning tumourous tissues and functioning liver cells within the tumour boundary. The 3D analysis from plain and CECT images was carried out on five types of liver lesions viz. HCC, metastasis, hemangioma, cyst, and abscess along with normal liver. The study resulted in clear delineation of functional liver tissues from non-functioning tumourous tissues within the tumour boundary from CECT as well as plain CT images. The volume of liver calculated using the proposed approach is found comparable with that obtained using Myrian-XP, a currently followed surgery planning tool in clinical practice. The obtained results from plain CT images will undoubtedly provide valuable diagnostic assistance and surgery planning even for the subset of patients for whom CECT acquisition is not advisable.

Novel fusion strategy for image fusion using rescue hunt optimization-based modified guidance model

ABSTRACT A new method for image fusion introduces an inventive strategy for amalgamating data from multiple images, resulting in the creation of a single, improved output image. This approach aims to address the limitations and challenges associated with conventional fusion techniques, paving the way for improved results in various applications. In this research, a novel approach for image fusion is presented, featuring a rescue hunt optimisation-based modified guidance model (RHO-based MG model). The methodology leverages the non-subsampled contourlet transform (NSCT) and non-subsampled shear let transform (NSST) to construct the fusion transform using two input images, typically infrared and visual images. By hybridising high-frequency (HF) and low-frequency (LF) bands from both types of images, the fusion model generates the final HF and LF bands. A distinctive modified guidance strategy is employed in the development of these bands. The proposed approach utilises a rescue hunt algorithm developed through the combination of search and rescue optimisation (SARO) and grey wolf optimisation (GWO) behaviours. In image fusion, optimisation fine-tunes VGG-19 and RESNET 18 models to improve their ability to combine multiple images effectively. This process involves adjusting the models’ internal parameters using optimisation techniques. By analysing the features in different input images, these models learn to extract meaningful information and create a fused image that retains the important details from each source. This strategy is further enhanced by integrating the VGG-19 and RESNET 18 models. The fused image is composed of combined HF and LF bands, with the final result obtained through an inverse hybrid transform. Experimental results, conducted on a dataset of 25 images, demonstrate the effectiveness of the approach with metrics average gradient (AG), edge intensity (EI), PSNR, RMSE, SSIM, and variance attained the values of 24.71, 236.67, 54.96 dB, 0.22, 63.16, and 0.11, respectively. This innovative method offers a promising direction for enhancing image fusion quality and is highlighted by its unique integration of optimisation and guidance strategies.

High quality underwater computational ghost imaging based on speckle decomposition and fusion of reconstructed images

Ghost imaging (GI) is an imaging method that reconstructs object information via light-intensity correlation measurements. The unconventional imaging mechanism of GI makes it suitable for imaging in scattering medium, for instance, turbid liquid, biological tissues, etc. However, the strong background noise in the reconstructed images is still the bottleneck problems. In this paper, we focus on the improvements of the reconstructions in underwater GI and propose a speckle decomposition and fusion method for suppressing the noise. The approach utilized computational ghost imaging framework and dictionary learning to acquire high- and low-frequency components that are better suited for image feature-based reconstruction. Furthermore, once the suitable threshold has been established, the speckles captured from the reference path are decomposed using the Nonsubsampled Contourlet Transform (NSCT). The acquired high- and low-frequency are then correlated with the overall optical intensity that captured by the bucket detectors. Ultimately, a multi-scale inverse NSCT transformation is employed to produce the final fusion-reconstructed image. The quantitative evaluation of the reconstructed image quality was conducted. In contrast to previous methodologies, the proposed method offers significant enhancements of the underwater GI imaging quality.

Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images

Noise and blurring in light microscope images are representative factors that affect accurate identification of cellular and subcellular structures in biological research. In this study, a method for l1-norm-based blind deconvolution after noise reduction with non-subsampled contourlet transform (NSCT) was designed and applied to a light microscope image to analyze its feasibility. The designed NSCT-based algorithm first separated the low- and high-frequency components. Then, the restored microscope image and the deblurred and denoised images were compared and evaluated. In both the simulations and experiments, the average coefficient of variation (COV) value in the image using the proposed NSCT-based algorithm showed similar values compared to the denoised image; moreover, it significantly improved the results compared with that of the degraded image. In particular, we confirmed that the restored image in the experiment improved the COV by approximately 2.52 times compared with the deblurred image, and the NSCT-based proposed algorithm showed the best performance in both the peak signal-to-noise ratio and edge preservation index in the simulation. In conclusion, the proposed algorithm was successfully modeled, and the applicability of the proposed method in light microscope images was proved based on various quantitative evaluation indices.

Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion

Objective. Multimodal medical image fusion (MMIF) technologies merges diverse medical images with rich information, boosting diagnostic efficiency and accuracy. Due to global optimization and single-valued nature, convolutional sparse representation (CSR) outshines the standard sparse representation (SR) in significance. By addressing the challenges of sensitivity to highly redundant dictionaries and robustness to misregistration, an adaptive convolutional sparsity scheme with measurement of the sub-band correlation in the non-subsampled contourlet transform (NSCT) domain is proposed for MMIF. Approach. The fusion scheme incorporates four main components: image decomposition into two scales, fusion of detail layers, fusion of base layers, and reconstruction of the two scales. We solved a Tikhonov regularization optimization problem with source images to obtain the base and detail layers. Then, after CSR processing, detail layers were sparsely decomposed using pre-trained dictionary filters for initial coefficient maps. NSCT domain’s sub-band correlation was used to refine fusion coefficient maps, and sparse reconstruction produced the fused detail layer. Meanwhile, base layers were fused using averaging. The final fused image was obtained via two-scale reconstruction. Main results. Experimental validation of clinical image sets revealed that the proposed fusion scheme can not only effectively eliminate the interference of partial misregistration, but also outperform the representative state-of-the-art fusion schemes in the preservation of structural and textural details according to subjective visual evaluations and objective quality evaluations. Significance. The proposed fusion scheme is competitive due to its low-redundancy dictionary, robustness to misregistration, and better fusion performance. This is achieved by training the dictionary with minimal samples through CSR to adaptively preserve overcompleteness for detail layers, and constructing fusion activity level with sub-band correlation in the NSCT domain to maintain CSR attributes. Additionally, ordering the NSCT for reverse sparse representation further enhances sub-band correlation to promote the preservation of structural and textural details.

A dual nonsubsampled contourlet network for synthesis images and infrared thermal images denoising

The most direct way to find the electrical switchgear fault is to use infrared thermal imaging technology for temperature measurement. However, infrared thermal imaging images are usually polluted by noise, and there are problems such as low contrast and blurred edges. To solve these problems, this article proposes a dual convolutional neural network model based on nonsubsampled contourlet transform (NSCT). First, the overall structure of the model is made wider by combining the two networks. Compared with the deeper convolutional neural network, the dual convolutional neural network (CNN) improves the denoising performance without increasing the computational cost too much. Secondly, the model uses NSCT and inverse NSCT to obtain more texture information and avoid the gridding effect. It achieves a good balance between noise reduction performance and detail retention. A large number of simulation experiments show that the model has the ability to deal with synthetic noise and real noise, which has high practical value.

Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition for Enhanced Imperceptibility and Robustness

Digital multimedia elements such as text, image, audio, and video can be easily manipulated because of the rapid rise of multimedia technology, making data protection a prime concern. Hence, copyright protection, content authentication, and integrity verification are today’s new challenging issues. To address these issues, digital image watermarking techniques have been proposed by several researchers. Image watermarking can be conducted through several transformations, such as discrete wavelet transform (DWT), singular value decomposition (SVD), orthogonal matrix Q and upper triangular matrix R (QR) decomposition, and non-subsampled contourlet transform (NSCT). However, a single transformation cannot simultaneously satisfy all the design requirements of image watermarking, which makes a platform to design a hybrid invisible image watermarking technique in this work. The proposed work combines four-level (4L) DWT and two-level (2L) SVD. The Arnold map initially encrypts the watermark image, and 2L SVD is applied to it to extract the s components of the watermark image. A 4L DWT is applied to the host image to extract the LL sub-band, and then 2L SVD is applied to extract s components that are embedded into the host image to generate the watermarked image. The dynamic-sized watermark maintains a balanced visual impact and non-blind watermarking preserves the quality and integrity of the host image. We have evaluated the performance after applying several intentional and unintentional attacks and found high imperceptibility and improved robustness with enhanced security to the system than existing state-of-the-art methods.

Non-Subsampled Contourlet Transform and Ground-Truth Score Generation Based Quality Assessment for DIBR-Synthesized Views

Non-Subsampled Contourlet Transform and Ground-Truth Score Generation Based Quality Assessment for DIBR-Synthesized Views

Multiresolution Interpretable Contourlet Graph Network for Image Classification.

Modeling contextual relationships in images as graph inference is an interesting and promising research topic. However, existing approaches only perform graph modeling of entities, ignoring the intrinsic geometric features of images. To overcome this problem, a novel multiresolution interpretable contourlet graph network (MICGNet) is proposed in this article. MICGNet delicately balances graph representation learning with the multiscale and multidirectional features of images, where contourlet is used to capture the hyperplanar directional singularities of images and multilevel sparse contourlet coefficients are encoded into graph for further graph representation learning. This process provides interpretable theoretical support for optimizing the model structure. Specifically, first, the superpixel-based region graph is constructed. Then, the region graph is applied to code the nonsubsampled contourlet transform (NSCT) coefficients of the image, which are considered as node features. Considering the statistical properties of the NSCT coefficients, we calculate the node similarity, i.e., the adjacency matrix, using Mahalanobis distance. Next, graph convolutional networks (GCNs) are employed to further learn more abstract multilevel NSCT-enhanced graph representations. Finally, the learnable graph assignment matrix is designed to get the geometric association representations, which accomplish the assignment of graph representations to grid feature maps. We conduct comparative experiments on six publicly available datasets, and the experimental analysis shows that MICGNet is significantly more effective and efficient than other algorithms of recent years.

Infrared and Visible Light Image Fusion Based on Image Enhancement and Secondary Nonsubsampled Contourlet Transform

Infrared and Visible Light Image Fusion Based on Image Enhancement and Secondary Nonsubsampled Contourlet Transform

Working condition perception for froth flotation based on NSCT multiscale features

Visual features at the froth surface of a flotation cell are closely related to the process condition and performance. Accurate identification of flotation conditions is crucial. However, the multi-scale properties of froth images are not all extracted based on existing perception methods. Considering that multi-scale frequency domain analysis can obtain more informative statistical froth features, a new working condition recognition based on nonsubsampled contourlet transform (NSCT) multiscale features is proposed and successfully validated by industrial froth images taken under different process conditions. NSCT was used to enhance the froth image, including the decomposition and reconstruction, contrast-limited adaptive histogram equalization (CLAHE), and multi-scale features of the enhanced image are extracted and analyzed. The fuzzy binarization method is adopted for extracting the morphological feature. Texture feature parameters are extracted from other high-frequency images. The particle swarm optimization support vector machine is used to recognize the working condition. The results indicate that the proposed algorithms, in particular the multiscale features classification based on NSCT, have the highest classification accuracy (93.33 %), and can accurately and reliably identify the working condition in the actual froth images.

GIS partial discharge data enhancement method based on self attention mechanism VAE-GAN

The reliability of geographic information system (GIS) partial discharge fault diagnosis is crucial for the safe and stable operation of power grids. This study proposed a data enhancement method based on a self-attention mechanism to optimize the VAE-GAN method and solve the problem of the lack of partial discharge samples and the unbalanced distribution between different defects. First, the non-subsampled contourlet transform (NSCT) algorithm was used to fuse the UHF and optical partial discharge signals to obtain a photoelectric fusion phase resolved partial discharge (PRPD) spectrum with richer information. Subsequently, the VAE structure was introduced into the traditional GAN, and the excellent hidden layer feature extraction ability of the VAE was used to guide the generation of the GAN. Then, the self-attention mechanism was integrated into the VAE-GAN, and the Wasserstein distance and gradient penalty mechanisms were used to optimize the network loss function and expand the sample sets to an equilibrium state. Finally, the KAZE and polar coordinate distribution entropy methods were used to extract the expanded samples. The eigenvectors of the sets were substituted into the long short-term memory (LSTM) network for partial discharge fault diagnosis. The experimental results show that the sample generation quality and fault diagnosis results of this method were significantly better than the traditional data enhancement method. The structure similarity index measure (SSIM) index is increased by 4.5% and 21.7%, respectively, and the average accuracy of fault diagnosis is increased by 22.9%, 9%, 5.7%, and 6.5%, respectively. The data enhancement method proposed in this study can provide a reference for GIS partial discharge fault diagnosis.

Multimodality medical image fusion analysis with multi-plane features of PET and MRI images using ONSCT

ABSTRACT The Multimodal Medical Image Fusion (MMIF) is affected by poor image quality, which leads to the extraction of inefficient features. The main intent of this work is to fuse various planes in the PET and MRI medical images efficiently using the MMIF approach. Initially, the sample images containing the axial plane of PET and MRI images are aggregated from standard datasets. Then, the collected images are employed for the decomposition process, which is accomplished via Optimal Non-Subsampled Contourlet Transform (ONSCT). The parameters in the NSCT are optimized using the Modified Water Strider Algorithm (MWSA. Once the images are decomposed, it is segmented into two sub-bands as high frequency and low-frequency sub-bands. Consequently, the high-frequency sub-bands of both PET and MRI images are fused by using the optimal weighted average fusion, in which the weight factor is obtained optimally by the MWSA algorithm. Similarly, the low-frequency sub-bands of both medical images are combined by sparse fusion technique. Finally, both the resultant fused images are subjected to Inverse Non-Subsampled Contourlet Transform (INSCT) to get desired fused images. The experimental findings suggest that the proposed model has effectively fused the images, and it also enhances the similarity score with axial planes.