Non-subsampled Shearlet Transform Research Articles

An Automated Brain Image Analysis System for Brain Cancer using Shearlets

In this paper, an Automated Brain Image Analysis (ABIA) system that classifies the Magnetic Resonance Imaging (MRI) of human brain is presented. The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis. The Non-Subsampled Shearlet Transform (NSST) that captures more visual information than conventional wavelet transforms is employed for feature extraction. As the feature space of NSST is very high, a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies. A combination of features that includes Gray Level Co-occurrence Matrix (GLCM) based features, Histograms of Positive Shearlet Coefficients (HPSC), and Histograms of Negative Shearlet Coefficients (HNSC) are estimated. The combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers; k-Nearest Neighbor (kNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers. The output of individual trained classifiers for a testing input is hybridized to take a final decision. The quantitative results of ABIA system on Repository of Molecular Brain Neoplasia Data (REMBRANDT) database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.

Fusion of Domestic High Resolution Remote Sensing Images Based on the Non-Subsampled Shearlet Transform

Fusion of Domestic High Resolution Remote Sensing Images Based on the Non-Subsampled Shearlet Transform

Multi-Modal Medical Image Fusion Using 3-Stage Multiscale Decomposition and PCNN with Adaptive Arguments

In the current era of technological development, medical imaging plays an important role in many applications of medical diagnosis and therapy. In this regard, medical image fusion could be a powerful tool to combine multi-modal images by using image processing techniques. But, conventional approaches failed to provide the effective image quality assessments and robustness of fused image. To overcome these drawbacks, in this work three-stage multiscale decomposition (TSMSD) using pulse-coupled neural networks with adaptive arguments (PCNN-AA) approach is proposed for multi-modal medical image fusion. Initially, nonsubsampled shearlet transform (NSST) is applied onto the source images to decompose them into low frequency and high frequency bands. Then, low frequency bands of both the source images are fused using nonlinear anisotropic filtering with discrete Karhunen–Loeve transform (NLAF-DKLT) methodology. Next, high frequency bands obtained from NSST are fused using PCNN-AA approach. Now, fused low frequency and high frequency bands are reconstructed using NSST reconstruction. Finally, band fusion rule algorithm with pyramid reconstruction is applied to get final fused medical image. Extensive simulation outcome discloses the superiority of proposed TSMSD using PCNN-AA approach as compared to state-of-the-art medical image fusion methods in terms of fusion quality metrics such as entropy (E), mutual information (MI), mean (M), standard deviation (STD), correlation coefficient (CC) and computational complexity.

Pan-Sharpening for Spectral Details Preservation Via Convolutional Sparse Coding in Non-Subsampled Shearlet Space

The optical satellite sensors encounter certain constraints on producing high-resolution multispectral (HRMS) images. Pan-sharpening (PS) is a remote sensing image fusion technique, which is an effective mechanism to overcome the limitations of available imaging products. The prevalent issue in PS algorithms is the imbalance between spatial quality and spectral details preservation, thereby producing intensity variations in the fused image. In this paper, a PS method is proposed based on convolutional sparse coding (CSC) implemented in the non-subsampled shearlet transform (NSST) domain. The source images, panchromatic (PAN) and multispectral (MS) images, are decomposed using NSST. The resultant high-frequency bands are fused using adaptive weights determined from chaotic grey wolf optimization (CGWO) algorithm. The CSC-based model is employed to fuse the low-frequency bands. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four datasets with different geographical content like urban area, vegetation, etc. and eight existing algorithms are used for evaluation of the proposed PS method. The comprehensive visual and quantitative results approve that the proposed method accomplishes considerable improvement in spatial and spectral details equivalence in the pan-sharpened image.

Convolutional neural network-based face recognition using non-subsampled shearlet transform and histogram of local feature descriptors

<span lang="EN-US">Face recognition has been using in a variety of applications like preventing retail crime, unlocking phones, smart advertising, finding missing persons, and protecting law enforcement. However, the ability of face recognition techniques reduces substantially because of changes in pose, illumination, and expressions of the individual. In this paper, a novel face recognition approach based on a non-subsampled shearlet transform (NSST), histogram-based local feature descriptors, and a convolutional neural network (CNN) is proposed. Initially, the Viola-Jones algorithm is used for face detection and then the extracted face region is preprocessed by image resizing operation. Then, NSST decomposes the input image into a low and high-frequency component image. The local feature descriptors such as local phase quantization (LPQ), pyramid of histogram of oriented gradients (PHOG), and the proposed CNN are used for extracting features from the low-frequency component of the NSST decomposition. The extracted features are fused to generate the feature vector and classified using support vector machine (SVM). The efficiency of the suggested method is tested on face databases like Olivetti Research Laboratory (ORL), Yale, and </span><span lang="IN">J</span><span lang="EN-US">apanese female facial expression</span><span lang="IN">(</span><span lang="EN-US">JAFFE</span><span lang="IN">)</span><span lang="EN-US">. The experimental outcomes reveal that the suggested face recognition method outperforms some of the state-of-the-art recognition approaches.</span>

Guided filter random walk and improved spiking cortical model based image fusion method in NSST domain

Image fusion has become a hot issue in the field of information processing. In this paper, a novel image fusion method based on guided filter random walk and improved spiking cortical model (ISCM) in non-subsampled shearlet transform (NSST) is presented. The core process is composed of three steps. Firstly, the source images to be fused are decomposed into the low-frequency parts and high-frequency parts via NSST. Then, two models including guided filter and random walk are combined to complete the fusion of low-frequency sub-images. As for the fusion of the high-frequency parts, the traditional spiking cortical model is improved to be ISCM to capture and fuse the details information of the source images. Finally, the fused image can be obtained by inverse NSST. In order to verify the effectiveness of the proposed method, lots of datasets with different categories are selected as the source images to conduct the simulation experiments. Experimental results demonstrate that the proposed method own obvious superiorities over the representative ones in terms of both subjective visual performance and objective evaluation data.

Combining Regional Energy and Intuitionistic Fuzzy Sets for Infrared and Visible Image Fusion.

To get more obvious target information and more texture features, a new fusion method for the infrared (IR) and visible (VIS) images combining regional energy (RE) and intuitionistic fuzzy sets (IFS) is proposed, and this method can be described by several steps as follows. Firstly, the IR and VIS images are decomposed into low- and high-frequency sub-bands by non-subsampled shearlet transform (NSST). Secondly, RE-based fusion rule is used to obtain the low-frequency pre-fusion image, which allows the important target information preserved in the resulting image. Based on the pre-fusion image, the IFS-based fusion rule is introduced to achieve the final low-frequency image, which enables more important texture information transferred to the resulting image. Thirdly, the ‘max-absolute’ fusion rule is adopted to fuse high-frequency sub-bands. Finally, the fused image is reconstructed by inverse NSST. The TNO and RoadScene datasets are used to evaluate the proposed method. The simulation results demonstrate that the fused images of the proposed method have more obvious targets, higher contrast, more plentiful detailed information, and local features. Qualitative and quantitative analysis results show that the presented method is superior to the other nine advanced fusion methods.

Statistical image watermark decoder using high-order difference coefficients and bounded generalized Gaussian mixtures-based HMT

Imperceptibility, robustness and data payload are three main requirements of any image watermarking systems to guarantee desired functionalities, but there is a tradeoff among them from the information-theoretic perspective. In order to achieve this balance, in this paper, we propose a new statistical image watermarking scheme in nonsubsampled Shearlet transform (NSST) domain, which is based on high-order difference coefficients and bounded generalized Gaussian mixture model (BGGMM) based hidden Markov tree (HMT). In embedding process, NSST is firstly performed on host image and NSST highpass subband is divided into no-overlapping blocks. Then high-order differences of NSST coefficient blocks are computed. And finally, watermark signal is inserted into robust high-order difference coefficients through non-linear multiplicative approach. In extraction phase, robust high-order difference coefficients are firstly modeled by employing BGGMM based HMT, which can capture accurately both marginal distributions and strong dependencies of high-order difference coefficients. Statistical model parameters are then estimated by combining the approach of minimizing the higher bound on data negative log-likelihood function and upward–downward algorithm. And finally, an image watermark decoder is developed using BGGMM based HMT and maximum likelihood decision. Experimental results on some test images and comparison with well-known existing methods demonstrate the efficacy and superiority of the proposed scheme.

GPR Image Noise Removal Using Grey Wolf Optimisation in the NSST Domain

Hyper-wavelet transforms, such as a non-subsampled shearlet transform (NSST), are one of the mainstream algorithms for removing random noise from ground-penetrating radar (GPR) images. Because GPR image noise is non-uniform, the use of a single fixed threshold for noisy coefficients in each sub-band of hyper-wavelet denoising algorithms is not appropriate. To overcome this problem, a novel NSST-based GPR image denoising grey wolf optimisation (GWO) algorithm is proposed. First, a time-varying threshold function based on the trend of noise changes in GPR images is proposed. Second, an edge area recognition and protection method based on the Canny algorithm is proposed. Finally, GWO is employed to select appropriate parameters for the time-varying threshold function and edge area protection method. The Natural Image Quality Evaluator is utilised as the optimisation index. The experiment results demonstrate that the proposed method provides excellent noise removal performance while protecting edge signals.

Multi-Sensor Fusion of Infrared and Visible Images Based on Modified Side Window Filter and Intensity Transformation

For multi-sensor fusion of infrared and visible images, it is difficult to retain the thermal radiation information of the infrared image and the texture information of the visible image in the fused image. To overcome this problem, a novel infrared and visible image fusion method based on a modified side window filter (MSWF) and an intensity transformation is proposed. First, an MSWF with effective edge-preservation ability is developed by adding four additional kernels to better decompose the source images to obtain the base and detail layers. Furthermore, to extract the edge information of the source images, we propose to further decompose the base layers to obtain the low-frequency layers and high-frequency layers (edge information) through the non-subsampled shearlet transform (NSST). Then, an S-shape intensity transformation function (ITF) is proposed to enhance the saliency information and suppress the non-saliency information in the infrared image. In the fusion process, considering the characteristics of the decomposed components, different fusion rules are designed to obtain the fused detail layer and low- and high-frequency layers. Finally, these fused components are reconstructed to obtain the final fusion image. It is experimentally demonstrated that the proposed method is superior to state-of-the-art fusion methods both in terms of subjective evaluation and objective metrics.

Medical image fusion with convolutional neural network in multiscale transform domain

Multimodal medical image fusion approaches have been commonly used to diagnose diseases and involve merging multiple images of different modes to achieve superior image quality and to reduce uncertainty and redundancy in order to increase the clinical applicability. In this paper, we proposed a new medical image fusion algorithm based on a convolutional neural network (CNN) to obtain a weight map for multiscale transform (curvelet/ non-subsampled shearlet transform) domains that enhance the textual and edge property. The aim of the method is achieving the best visualization and highest details in a single fused image without losing spectral and anatomical details. In the proposed method, firstly, non-subsampled shearlet transform (NSST) and curvelet transform (CvT) were used to decompose the source image into low-frequency and high-frequency coefficients. Secondly, the low-frequency and high-frequency coefficients were fused by the weight map generated by Siamese Convolutional Neural Network (SCNN), where the weight map get by a series of feature maps and fuses the pixel activity information from different sources. Finally, the fused image was reconstructed by inverse multi-scale transform (MST). For testing of proposed method, standard gray-scaled magnetic resonance (MR) images and colored positron emission tomography (PET) images taken from Brain Atlas Datasets were used. The proposed method can effectively preserve the detailed structure information and performs well in terms of both visual quality and objective assessment. The fusion experimental results were evaluated (according to quality metrics) with quantitative and qualitative criteria.

A non-subsampled Shearlet transform based approach for heartbeat sound watermarking

To secure the heartbeat sound exchanged in telemedicine, we propose in this work a blind watermarking approach combining a Non-Subsampled Shearlet Transform and Singular Value Decomposition. This approach allows guaranteeing the patients' successful authentication by integrating a watermark containing the patient's specific information as well as his photography in the audio file. In this approach to decompose the signal into low and high frequencies, a Non-Subsampled Shearlet Transform is performed. Singular value decomposition is then applied to the obtained low frequency components and the watermark bits are integrated by modulating the singular values. The imperceptibility results have typically shown our approach can integrate a satisfactory amount of information while preserving the diagnostic content of the audio file. In addition, the robustness tests demonstrate the proposed approach can efficiently generate a watermark resisting to several attacks. The imperceptibility results have shown our approach can integrate a satisfactory amount of necessary information while preserving the diagnostic content of the audio file. Since the patient informations are embedded in the multimedia file, a watermark extraction will clearly authenticate the potential patient. Since the patient informations are embedded in the multimedia file, a watermark extraction will clearly authenticate the patient.

Utilization of image interpolation and fusion in brain tumor segmentation.

Brain tumor is a mass of anomalous cells in the brain. Medical imagining techniques have a vital role in the diagnosis of brain tumors. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) techniques are the most popular techniques to localize the tumor area. Brain tumor segmentation is very important for the diagnosis of tumors. In this paper, we introduce a framework to perform brain tumor segmentation, and then localize the region of the tumor, accurately. The proposed framework begins with the fusion of MR and CT images by the Non-Sub-Sampled Shearlet Transform (NSST) with the aid of the Modified Central Force Optimization (MCFO) to get the optimum fusion result from the quality metrics perspective. After that, image interpolation is applied to obtain a High-Resolution (HR) image from the Low-Resolution (LR) ones. The objective of the interpolation process is to enrich the details of the fusion result prior to segmentation. Finally, the threshold and the watershed segmentation are applied sequentially to localize the tumor region, clearly. The proposed framework enhances the efficiency of segmentation to help the specialists diagnose brain tumors.

Multi-modal medical image fusion framework using co-occurrence filter and local extrema in NSST domain

The fusion of vital imaging information has turned into a primary issue for biomedical applications. In this paper, a new multi-modality medical image fusion method is proposed in the shearlet domain. In the proposed algorithm, input images are decomposed using Non-subsampled shearlet transform (NSST) to get low and high frequencies components. A novel procedure to decompose and combine the base layers and detail layers using the local extrema (LE) approach is used and fused using a Co-occurrence filter (CoF) in low-frequency components. In high-frequency components, an edge-preserving image fusion strategy is performed using sum modified Laplacian (SML) to combine the high-frequency coefficients. The experimental outcomes and comparative analysis are performed over the Multi-modal medical image dataset using proposed and existing methods. It is exhibited through test outcomes and assessments that the proposed technique beats the cutting-edge fusion strategies concerning edge preservation in subjective and objective assessment criteria.

Color texture image retrieval based on Copula multivariate modeling in the Shearlet domain

Due to the increasing number of images on the Internet, image retrieval framework is needed for image-based search. Shearlet transform is a sparse, multiscale, and multidimensional representation used to extract anisotropic features in image processing applications such as image fusion and image denoising. In this paper, we proposed a color texture image retrieval framework based on Shearlet domain modeling using Copula multivariate model. In the proposed framework, Gaussian Copula is used to model the dependencies between different sub-bands of the Non-Subsample Shearlet Transform (NSST) and non-Gaussian models are used for marginal modeling of the coefficients. Six different schemes are proposed for modeling NSST coefficients based on the four types of neighboring defined; moreover, Kullback–Leibler Divergence(KLD) close form is calculated in different situations for the two Gaussian Copula and non-Gaussian functions in order to investigate the similarities in the proposed retrieval framework. The Jeffery divergence (JD) criterion, which is a symmetrical version of KLD, is used for investigating similarities in the proposed framework. We have implemented our experiments on four texture image retrieval benchmark datasets, the results of which show the superiority of the proposed framework over the existing state-of-the-art methods. In addition, the retrieval time of the proposed framework is also analyzed in the two steps of feature extraction and similarity matching, which also shows that the proposed framework enjoys an appropriate retrieval time.

Single image super-resolution via hybrid resolution NSST prediction

Convolutional neural networks (CNNs) have achieved great success in single image super-resolution (SR). However, most previous methods predict high-resolution (HR) images in the spatial domain, producing over-smoothed outputs while losing texture details. To address this problem, in this paper we propose to predict nonsubsampled shearlet transform (NSST) coefficients, which better represent the global topology information and local texture details of HR images. On the other hand, we propose a deep hybrid resolution network by a residual-in-residual style, which aggregates features of multiple resolutions so as to gather rich context information in compact representations. When evaluated on a newly released RealSR dataset and traditional simulated datasets, our method, namely hybrid resolution NSST prediction (HRNP), achieves more appealing results, w.r.t. PSNR and SSIM, than the state-of-the-art methods. Moreover, we find our HRNP is more capable of preserving complex edges and curves than other methods.

Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion.

The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource image fusion is still challenging. This paper proposes a novel multisource remote sensing image fusion algorithm. It integrates the contrast saliency map (CSM) and the sum-modified-Laplacian (SML) in the nonsubsampled shearlet transform (NSST) domain. The NSST is utilized to decompose the source images into low-frequency sub-bands and high-frequency sub-bands. Low-frequency sub-bands reflect the contrast and brightness of the source images, while high-frequency sub-bands reflect the texture and details of the source images. Using this information, the contrast saliency map and SML fusion rules are introduced into the corresponding sub-bands. Finally, the inverse NSST reconstructs the fusion image. Experimental results demonstrate that the proposed multisource remote image fusion technique performs well in terms of contrast enhancement and detail preservation.

RETRACTED ARTICLE: MAMIF: multimodal adaptive medical image fusion based on B-spline registration and non-subsampled shearlet transform

Off late, medical image fusion has emerged as an inspiring approach in merging different modalities of medical images. The fused image helps the medicos to diagnose various critical diseases quickly and precisely. This paper proposes two fusion algorithim named Multimodal Adaptive Medical Image Fusion (MAMIF) and Multimodal without Denoised Medical Image Fusion (MDMIF) and both of the method uses Non-Subsampled Shearlet Transform (NSST) and B-spline registration model. However as MAMIF uses denoise method, it provides better visually enhanced images. The presented MAMIF algorithim fuses the images without losing any vital information for the given set of real-time and public datasets. The entire fusion framework uses features extracted from NSST decomposed images by using Human Visual System (HVS) based Low Frequency (LF) sub-band fusion and Log-Gabor energy-based High Frequency (HF) sub-band fusion. The proposed framework is agnostic of source image size (pairs should be of the same size). The experiments were carried out leveraging 14 sets of image dataset that includes grayscale and color images. The performance calculation of the proposed MAMIF is evaluated based on the dataset collected from HCG hospital, Bangalore, and further validated by radiologists from the same hospital. Comparing the simulated results, the proposed adaptive model MAMIF produced superior visually fused images compared to other approaches such as MDMIF and MMDWT.

An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion

The fast-developing Image fusion technique has become a necessary one in every field. Analyzing the efficiency of various fusion technologies analytically and objectively are spotted as an essentially required processes. Further, Image fusion becomes an inseparable technique in the medical field, since the role of medical images in diagnosing and identifying diseases becomes a crucial task for the radiologists and doctors at its early stage. Different modalities used in clinical applications offer unique information, unlike any other in any form. To diagnose diseases with high accuracy, clinicians require data from more than one modality. Multimodal image fusion has received wide popularity in the medical field since it enhances the accuracy of the clinical diagnosis thereby fusing the complementary information present in more than one image. Obtaining optimal value along with a reduction in cost and time in multimodal medical image fusions are a critical one. Here, in this paper a new multi-modality algorithm for medical image fusion based on the Adolescent Identity Search Algorithm (AISA) for the Non-Subsampled Shearlet Transform is proposed to obtain image optimization and to reduce the computational cost and time. The NSST is a multi-directional and multi-dimensional example of a multiscale and multi-directional wavelet transform. The input source image is decomposed into the NSST subbands at the initial stage. The boundary measure is modulated by the Adolescent Identity Search Algorithm (AISA) that fuses the sub-band in the NSST thereby reducing the complexity and increasing the computational speed. The proposed method is tested under different real-time disease datasets such as Glioma, mild Alzheimer's, and Encephalopathy with hypertension that includes similar pairs of images and analyzed different evaluation measures such as Entropy, standard deviation, structural similarity index measure,Mutual information, Average gradient, Xydeas and Petrovic metric, Peak-signal to-noise-ratio, processing time. The experimental findings and discussions indicate that the proposed algorithm outperforms other approaches and offers high quality fused images for an accurate diagnosis.

MRI denoising using advanced NLM filtering with non-subsampled shearlet transform

Many noise removal algorithms have been introduced so far for denoising of MRI images like a bilateral filter, wavelet transform, maximum likelihood, non-local means, etc. All the existing methods suffer from some drawbacks or limitations. This research proposed a novel denoising technique for MRI images based on the advanced NLM method with non-subsampled shearlet transform (NSST). NSST is a combination of non-subsampled pyramid filters and non-subsampled shearing filters. NLM filter is modified for better noise detection and calculation of denoised value. The parameters are set to provide maximum output and high-quality denoising. The proposed methods are compared with existing state-of-the-art methods like standard NLM filter, fast NLM, LMMSE, NLML, etc. The analysis is based on values of PSNR, SSIM and RMSE. The results show that the proposed methods remove the noise much effectively as compared to existing denoising methods.