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Journal articles on the topic 'NSST'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Zhang, Kang, Yongdong Huang, and Cheng Zhao. "Remote sensing image fusion via RPCA and adaptive PCNN in NSST domain." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 05 (2018): 1850037. http://dx.doi.org/10.1142/s0219691318500376.

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In order to improve fused image quality of multi-spectral (MS) image and panchromatic (PAN) image, a new remote sensing image fusion algorithm based on robust principal component analysis (RPCA) and non-subsampled shearlet transform (NSST) is proposed. First, the first principle component PC1 of MS image is extracted via principal component analysis (PCA). Then, the component PC1 and PAN image are decomposed by NSST to get the low and high frequency subbands, respectively. For the low frequency subband, the sparse matrix of PAN image by RPCA decomposition is used to guide the fusion rule; for

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Guseinov, I. I. "Quantum Self-Frictional Relativistic Nucleoseed Spinor-Type Tensor Field Theory of Nature." Advances in High Energy Physics 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6049079.

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For study of quantum self-frictional (SF) relativistic nucleoseed spinor-type tensor (NSST) field theory of nature (SF-NSST atomic-molecular-nuclear and cosmic-universe systems) we use the complete orthogonal basis sets of22s+1-component column-matrices type SFΨnljmjδ⁎s-relativistic NSST orbitals (Ψδ⁎s-RNSSTO) and SFXnljmjs-relativistic Slater NSST orbitals (Xs-RSNSSTO) through theψnlmlδ⁎-nonrelativistic scalar orbitals (ψδ⁎-NSO) andχnlml-nonrelativistic Slater type orbitals (χ-NSTO), respectively. Hereδ⁎=pl⁎orδ⁎=α⁎andpl⁎=2l+2-α⁎, α⁎are the integer(α⁎=α, -∞<α≤2) or noninteger(α⁎≠α, -∞<α⁎

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Baruah, Hilly Gohain, Vijay Kumar Nath, Deepika Hazarika, and Rakcinpha Hatibaruah. "Local bit-plane neighbour dissimilarity pattern in non-subsampled shearlet transform domain for bio-medical image retrieval." Mathematical Biosciences and Engineering 19, no. 2 (2021): 1609–32. http://dx.doi.org/10.3934/mbe.2022075.

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<abstract><p>This paper introduces a novel descriptor non-subsampled shearlet transform (NSST) local bit-plane neighbour dissimilarity pattern (NSST-LBNDP) for biomedical image retrieval based on NSST, bit-plane slicing and local pattern based features. In NSST-LBNDP, the input image is first decomposed by NSST, followed by introduction of non-linearity on the NSST coefficients by computing local energy features. The local energy features are next normalized into 8-bit values. The multiscale NSST is used to provide translational invariance and has flexible directional sensitivity t

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Donlifack Atemkeng, Cyrille, Arnaud Kamdem Tamo, Giscard Doungmo, Liouna Adoum Amola, Julio Jimmy Kouanang Ngouoko, and Théophile Kamgaing. "Thermodynamic, Nonlinear Kinetic, and Isotherm Studies of Bisphenol A Uptake onto Chemically Activated Carbons Derived from Safou (Dacryodes edulis) Seeds." Journal of Chemistry 2022 (October 7, 2022): 1–17. http://dx.doi.org/10.1155/2022/7717148.

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The interest of this work is to evaluate the possibility of using safou seeds to develop a new low-cost adsorbent and study its application to remove bisphenol A from an aqueous solution for a sustainable and ecological use of this biomass. This was done by optimizing some parameters that influence the adsorption process. The central composite design with four centre points was used to optimize the process variables. The concentration of bisphenol A solution, adsorbent dosage, stirring time, and solution pH on the adsorption capacity were considered, while the response measured was the quantit

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Jampani, Ravi, and Narmadha R. "Image Fusion Based on Nonsubsampled Shearlet Transform." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 4177–80. https://doi.org/10.35940/ijeat.C5452.029320.

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In current days, an image fusion is a powerful method and developing field in the area of image processing. The image fusion is the process of combining two or more images into a single image then the resulting image will appear more informative than any of the input images. It is the process of assimilation of numerous input images into a new single fused image with highly informative than the input image. There are various image fusion transform techniques are proposed. Out of that techniques a Non-subsampled shearlet transform includes shift invariant property, highly directionality, feasib

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Shah, Syed Wajid Ali, Asif Ali Laghari, Muhammad Shakir, Geng Tong, and Shahid Karim. "Infrared and visible image fusion based on improved NSCT and NSST." International Journal of Electronic Security and Digital Forensics 1, no. 1 (2024): 1. http://dx.doi.org/10.1504/ijesdf.2024.10054426.

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Karim, Shahid, Geng Tong, Muhammad Shakir, Asif Ali Laghari, and Syed Wajid Ali Shah. "Infrared and visible image fusion based on improved NSCT and NSST." International Journal of Electronic Security and Digital Forensics 16, no. 3 (2024): 284–303. http://dx.doi.org/10.1504/ijesdf.2024.138334.

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Rahman, Md Mizanur, Xueting Zhang, K. Hasan, and Sheng Chen. "RANS Flow Computation around Transonic RAE2822 Airfoil with a New SST Turbulence Model." MIST INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY 12 (December 26, 2024): 23–27. https://doi.org/10.47981/j.mijst.12(02)2024.477(23-27).

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The solution to RANS (Reynolds-averaged Navier-Stokes) equations invokes a suitable framework for turbulence modelling. To account for turbulence and transition effects, a new SST (Shear Stress Transport) k-ω turbulence model is coupled with RANS to simulate the transonic flow passing an RAE2822 air foil. Three sets of experimental data of the super-critical RAE2822 air foil are employed to validate the new SST (NSST) closure. Computations are conducted for a limited range of Reynolds numbers with variable angle of attack. The NSST model has been found to replicate satisfactory results for lif

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Li, Liangliang, Yujuan Si, Linli Wang, Zhenhong Jia, and Hongbing Ma. "Brain Image Enhancement Approach Based on Singular Value Decomposition in Nonsubsampled Shearlet Transform Domain." Journal of Medical Imaging and Health Informatics 10, no. 8 (2020): 1785–94. http://dx.doi.org/10.1166/jmihi.2020.3111.

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In this work, a novel image enhancement algorithm using NSST and SVD is proposed to improve the definition of the acquired brain images. The input brain image is computed by CLAHE, then the processed brain image and input brain image are decomposed into low- and high-frequency components by NSST, the singular value matrix of the low-frequency component is estimated. The final enhancement image is obtained by inverse NSST. Results of this experiment demonstrate that the proposed technique has good performance in terms of brain image enhancement when compared to other methods.

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Qu, Zhi, Yaqiong Xing, and Yafei Song. "An Image Enhancement Method Based on Non-Subsampled Shearlet Transform and Directional Information Measurement." Information 9, no. 12 (2018): 308. http://dx.doi.org/10.3390/info9120308.

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Based on the advantages of a non-subsampled shearlet transform (NSST) in image processing and the characteristics of remote sensing imagery, NSST was applied to enhance blurred images. In the NSST transform domain, directional information measurement can highlight textural features of an image edge and reduce image noise. Therefore, NSST was applied to the detailed enhancement of high-frequency sub-band coefficients. Based on the characteristics of a low-frequency image, the retinex method was used to enhance low-frequency images. Then, an NSST inverse transformation was performed on the enhan

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Abdelfatih, Bengana, and Boukli Hacene Ismail. "An Adaptive Image Fusion Algorithm in the NSST Based on CDF 9/7 for Neurodegenerative Diseases." Traitement du Signal 39, no. 4 (2022): 1379–85. http://dx.doi.org/10.18280/ts.390432.

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The neurodegenerative disease such as: Parkinson's disease (PD), mild Alzheimer’s affects many people and has a serious influence on their life, With the quick advancement of computer-aided diagnostic (CAD) methods, early detection is crucial since effective treatment halts the spread of the disease. Image fusion is useful for medical diagnostics. In this paper we propose a multi-modality medical image fusion algorithm in NSST domain. Shearlets (NSST) are decomposed similarly to contourlets (NSCT), except that instead of applying the Laplacian pyramid followed by directional filtering, shearle

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Paul, André, Stefan Mulitza, Rüdiger Stein, and Martin Werner. "A global climatology of the ocean surface during the Last Glacial Maximum mapped on a regular grid (GLOMAP)." Climate of the Past 17, no. 2 (2021): 805–24. http://dx.doi.org/10.5194/cp-17-805-2021.

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Abstract. We present a climatology of the near-sea-surface temperature (NSST) anomaly and the sea-ice extent during the Last Glacial Maximum (LGM, 23 000–19 000 years before present) mapped on a global regular 1∘×1∘ grid. It is an extension of the Glacial Atlantic Ocean Mapping (GLAMAP) reconstruction of the Atlantic NSST based on the faunal and floral assemblage data of the Multiproxy Approach for the Reconstruction of the Glacial Ocean Surface (MARGO) project and several recent estimates of the LGM sea-ice extent. Such a gridded climatology is highly useful for the visualization of the LGM c

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Yuan, Min, Bingxin Yang, Yide Ma, Jiuwen Zhang, Runpu Zhang, and Caiyuan Zhang. "Compressed Sensing MRI Reconstruction from Highly Undersampledk-Space Data Using Nonsubsampled Shearlet Transform Sparsity Prior." Mathematical Problems in Engineering 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/615439.

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Compressed sensing has shown great potential in speeding up MR imaging by undersamplingk-space data. Generally sparsity is used as a priori knowledge to improve the quality of reconstructed image. Compressed sensing MR image (CS-MRI) reconstruction methods have employed widely used sparsifying transforms such as wavelet or total variation, which are not preeminent in dealing with MR images containing distributed discontinuities and cannot provide a sufficient sparse representation and the decomposition at any direction. In this paper, we propose a novel CS-MRI reconstruction method from highly

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Liu, Yanfang, Shiqiang Li, and Heng Zhang. "Multibaseline Interferometric Phase Denoising Based on Kurtosis in the NSST Domain." Sensors 20, no. 2 (2020): 551. http://dx.doi.org/10.3390/s20020551.

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Interferometric phase filtering is a crucial step in multibaseline interferometric synthetic aperture radar (InSAR). Current multibaseline interferometric phase filtering methods mostly follow methods of single-baseline InSAR and do not bring its data superiority into full play. The joint filtering of multibaseline InSAR based on statistics is proposed in this paper. We study and analyze the fourth-order statistical quantity of interferometric phase: kurtosis. An empirical assumption that the kurtosis of interferograms with different baselines keeps constant is proposed and is named as the bas

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15

Li, Liangliang, and Hongbing Ma. "Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion." Sensors 21, no. 5 (2021): 1756. http://dx.doi.org/10.3390/s21051756.

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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-frequen

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Diwakar, Manoj, Prabhishek Singh, Ravinder Singh, et al. "Multimodality Medical Image Fusion Using Clustered Dictionary Learning in Non-Subsampled Shearlet Transform." Diagnostics 13, no. 8 (2023): 1395. http://dx.doi.org/10.3390/diagnostics13081395.

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Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract both low- and high-frequency image components. A novel approach is proposed for fusing low-frequency components using a modified sum-modified Laplacian (MSML)-based clustered dictionary learning technique. In the NSST domain, directed contrast can be used to fuse high-frequency coefficients.

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Qu, Zhi, Yaqiong Xing, and Yafei Song. "Image Enhancement Based on Pulse Coupled Neural Network in the Nonsubsample Shearlet Transform Domain." Mathematical Problems in Engineering 2019 (February 21, 2019): 1–11. http://dx.doi.org/10.1155/2019/2641516.

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In this study, pulse coupled neural network (PCNN) was modified and applied to the enhancement of blur images. In the transform domain of nonsubsample shearlet transform (NSST), PCNN was used to enhance the details of images in the low- and high-frequency subbands, and then the enhanced low- and high-frequency coefficients were used for NSST inverse transformation to obtain the enhanced images. The results showed that the proposed method can produce higher-quality images and suppress noise better than traditional image enhancement strategies.

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Liu, Qiuyuan, Wendong Zhang, Juntao Jiang, et al. "Research on Microstructure and Corrosion Behavior of Zinc-Magnesium Coating by Powder Impregnation." Journal of Physics: Conference Series 2101, no. 1 (2021): 012057. http://dx.doi.org/10.1088/1742-6596/2101/1/012057.

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Abstract In order to improve the service life of railway fasteners under various environmental conditions, the Zn and Zn-Mg coating were prepared on the railway fastener gaskets by powder impregnation. XRD and SEM were used to characterize the phase composition, microstructure and morphology before and after the salt spray. The neutral salt spray test (NSST) and the electrochemical workstation were used to characterize the corrosion resistance of the two coatings. Results show that there was a large area of red rust on Zn coating after 480 hours of NSST, while there was no red rust on Zn-Mg co

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Zhang, Rui, Zhongyang Wang, Haoze Sun, Lizhen Deng, and Hu Zhu. "TDFusion: When Tensor Decomposition Meets Medical Image Fusion in the Nonsubsampled Shearlet Transform Domain." Sensors 23, no. 14 (2023): 6616. http://dx.doi.org/10.3390/s23146616.

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In this paper, a unified optimization model for medical image fusion based on tensor decomposition and the non-subsampled shearlet transform (NSST) is proposed. The model is based on the NSST method and the tensor decomposition method to fuse the high-frequency (HF) and low-frequency (LF) parts of two source images to obtain a mixed-frequency fused image. In general, we integrate low-frequency and high-frequency information from the perspective of tensor decomposition (TD) fusion. Due to the structural differences between the high-frequency and low-frequency representations, potential informat

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Ding, Zhaisheng, Dongming Zhou, Rencan Nie, Ruichao Hou, and Yanyu Liu. "Brain Medical Image Fusion Based on Dual-Branch CNNs in NSST Domain." BioMed Research International 2020 (April 14, 2020): 1–15. http://dx.doi.org/10.1155/2020/6265708.

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Computed tomography (CT) images show structural features, while magnetic resonance imaging (MRI) images represent brain tissue anatomy but do not contain any functional information. How to effectively combine the images of the two modes has become a research challenge. In this paper, a new framework for medical image fusion is proposed which combines convolutional neural networks (CNNs) and non-subsampled shearlet transform (NSST) to simultaneously cover the advantages of them both. This method effectively retains the functional information of the CT image and reduces the loss of brain structu

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Lei, Yang, and Jing Ma. "Technique for Intrusion Detection Based on NSST Domain Artificial Neural Networks." Applied Mechanics and Materials 713-715 (January 2015): 2519–22. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2519.

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The issue of intrusion detection has been an active topic in both military and civilian areas, and a great many relevant algorithms and techniques have been developed accordingly. This paper addresses a novel technique based on non-subsampled shearlet transform (NSST) domain artificial neural networks (ANN) to solve the above problem, employing multi-scale geometry analysis (MGA) of NSST and the train characteristics of ANN together. Experimental results indicate that, compared with other existing conventional intrusion detection tools, the proposed one is superior to other current popular one

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Shi, Cheng, Fang Liu, and Qiguang Miao. "Pan-sharpening via regional division and NSST." Multimedia Tools and Applications 74, no. 18 (2014): 7843–57. http://dx.doi.org/10.1007/s11042-014-2027-x.

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Wu, Xuecheng, Houkui Zhou, Huimin Yu, et al. "A Method for Medical Microscopic Images’ Sharpness Evaluation Based on NSST and Variance by Combining Time and Frequency Domains." Sensors 22, no. 19 (2022): 7607. http://dx.doi.org/10.3390/s22197607.

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An algorithm for a sharpness evaluation of microscopic images based on non-subsampled shearlet wave transform (NSST) and variance is proposed in the present study for the purpose of improving the noise immunity and accuracy of a microscope’s image autofocus. First, images are decomposed with the NSST algorithm; then, the decomposed sub-band images are subjected to variance to obtain the energy of the sub-band coefficients; and finally, the evaluation value is obtained from the ratio of the energy of the high- and low-frequency sub-band coefficients. The experimental results show that the propo

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Jiao, Jiao, and Lingda Wu. "Pansharpening with a Gradient Domain GIF Based on NSST." Electronics 8, no. 2 (2019): 229. http://dx.doi.org/10.3390/electronics8020229.

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In order to improve the fusion quality of multispectral (MS) and panchromatic (PAN) images, a pansharpening method with a gradient domain guided image filter (GIF) that is based on non-subsampled shearlet transform (NSST) is proposed. First, multi-scale decomposition of MS and PAN images is performed by NSST. Second, different fusion rules are designed for high- and low-frequency coefficients. A fusion rule that is based on morphological filter-based intensity modulation (MFIM) technology is proposed for the low-frequency coefficients, and the edge refinement is carried out based on a gradient

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Yallamandaiah, S., and Purnachand N. "Convolutional neural network-based face recognition using non-subsampled shearlet transform and histogram of local feature descriptors." International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (2021): 1079–90. https://doi.org/10.11591/ijai.v10.i4.pp1079-1090.

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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 a

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Xing, Xiao Xue, Fu Cheng Cao, Wei Wei Shang, and Fu Liu. "A Novel Image Fusion Method Using Non-Subsampled Shearlet Transform." Applied Mechanics and Materials 668-669 (October 2014): 1033–36. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1033.

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As a novel MGA (Multiscale Geometric Analysis) tool, shearlet is equipped with a rich mathematical structure similar to wavelet. In this paper, a novel image fusion method using Non-subsampled Shearlet Transform is proposed. First, the source images are decomposed into low-pass and high-pass subbands using NSST. Second, the high-pass subbands coefficients of the images are fused according to the average gradient. Third, the low-pass subbands coefficients of the images are fused by the weighted regional entropy. Finally, the image is reconstructed by the inverse non-subsampled shearlet transfor

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Jin, Xin, Rencan Nie, Dongming Zhou, Quan Wang, and Kangjian He. "Multifocus Color Image Fusion Based on NSST and PCNN." Journal of Sensors 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/8359602.

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This paper proposed an effective multifocus color image fusion algorithm based on nonsubsampled shearlet transform (NSST) and pulse coupled neural networks (PCNN); the algorithm can be used in different color spaces. In this paper, we take HSV color space as an example, H component is clustered by adaptive simplified PCNN (S-PCNN), and then the H component is fused according to oscillation frequency graph (OFG) of S-PCNN; at the same time, S and V components are decomposed by NSST, and different fusion rules are utilized to fuse the obtained results. Finally, inverse HSV transform is performed

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Mu, Zhenhua, Ming Wang, Yihan Wang, Ruoxi Song, and Xianghai Wang. "SI2FM: SID Isolation Double Forest Model for Hyperspectral Anomaly Detection." Remote Sensing 15, no. 3 (2023): 612. http://dx.doi.org/10.3390/rs15030612.

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Hyperspectral image (HSI) anomaly detection (HSI-AD) has become a hot issue in hyperspectral information processing as a method for detecting undesired targets without a priori information against unknown background and target information, which can be better adapted to the needs of practical applications. However, the demanding detection environment with no prior and small targets, as well as the large data and high redundancy of HSI itself, make the study of HSI-AD very challenging. First, we propose an HSI-AD method based on the nonsubsampled shearlet transform (NSST) domain spectral inform

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Sun, Zengguo, Rui Shi, and Wei Wei. "Synthetic-Aperture Radar Image Despeckling based on Improved Non-Local Means and Non-Subsampled Shearlet Transform." Information Technology And Control 49, no. 3 (2020): 299–307. http://dx.doi.org/10.5755/j01.itc.49.3.23998.

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When Synthetic-Aperture (SAR) image is transformed into wavelet domain and other transform domains, most of the coefficients of the image are small or zero. This shows that SAR image is sparse. However, speckle can be seen in SAR images. The non-local means is a despeckling algorithm, but it cannot overcome the speckle in homogeneous regions and it blurs edge details of the image. In order to solve these problems, an improved non-local means is suggested. At the same time, in order to better suppress the speckle effectively in edge regions, the non-subsampled Shearlet transform (NSST) is appli

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Li, Bo, Hong Peng, Xiaohui Luo, et al. "Medical Image Fusion Method Based on Coupled Neural P Systems in Nonsubsampled Shearlet Transform Domain." International Journal of Neural Systems 31, no. 01 (2020): 2050050. http://dx.doi.org/10.1142/s0129065720500501.

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Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and parallel computing model, combining the spiking and coupled mechanisms of neurons. This paper focuses on how to apply CNP systems to handle the fusion of multi-modality medical images and proposes a novel image fusion method. Based on two CNP systems with local topology, an image fusion framework in nonsubsampled shearlet transform (NSST) domain is designed, where the two CNP systems are used to control the fusion of low-frequency NSST coefficients. The proposed fusion method is evaluated on 20 pairs of m

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Cho, Heetae, Dongoh Joo, and Kyle Maurice Woosnam. "Cross-cultural Validation of the Nostalgia Scale for Sport Tourism (NSST): A Multilevel Approach." Journal of Hospitality & Tourism Research 44, no. 4 (2020): 624–43. http://dx.doi.org/10.1177/1096348019899461.

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This study evaluated the cross-cultural generalizability of the Nostalgia Scale for Sport Tourism (NSST), which was originally developed and examined in the context of football tourists in the United States. Data were collected from baseball tourists in South Korea, and multilevel confirmatory factor analysis and multilevel structural equation modelling were used for data analysis. Results supported the reliability and the validity of the scale both at individual and group levels, revealing an identical five-factor structure across the 29-item scale. Additionally, this study found a significan

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Cho, Heetae, Hyun-Woo Lee, DeWayne Moore, William C. Norman, and Gregory Ramshaw. "A Multilevel Approach to Scale Development in Sport Tourist Nostalgia." Journal of Travel Research 56, no. 8 (2017): 1094–106. http://dx.doi.org/10.1177/0047287516683834.

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Nostalgia has been identified as an essential factor to understand sport tourists’ behavioral intentions. However, a measurement model to examine nostalgia has not been developed in the field of sport tourism. The purpose of this study was to develop a valid and reliable Nostalgia Scale for Sport Tourism (NSST) to measure sport tourists’ nostalgia. A multilevel analysis was used in order to avoid biases caused by common characteristics within a travel group. The scale conceptualized sport nostalgia as a five-dimensional construct reflecting sport tourists’ nostalgia of sport team, environment,

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Chaudhary, Dev Kumar, Prabhishek Singh, Achyut Shankar, and Manoj Diwakar. "M3IF-NSST-MTV: Modified Total variation-based multi-modal medical image fusion using Laplacian energy and morphology in the NSST domain." Image and Vision Computing 159 (June 2025): 105581. https://doi.org/10.1016/j.imavis.2025.105581.

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Li Quanjun, Zhang Guicang, and Han Genliang. "Image Fusion Based on NSST and CSR Under Robust Principal Component Analysis." Journal of Mathematics and Informatics 21 (2021): 53–64. http://dx.doi.org/10.22457/jmi.v21a05198.

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Aiming at the problems of loss of detail information and noise interference that are easy to produce in the image fusion process, a robust principal component analysis (RPCA) based on Convolutional Sparse Coding (CSR) and For image fusion of NonSubsampled Shear Wave Transform (NSST), the source image is pre-enhanced first; then the image is decomposed by RPCA to obtain low-rank images and sparse images; then NSST fusion is used respectively For low-rank images, CSR coding is used to fuse sparse images, and finally two separately fused images are synthesized to obtain the final fused image. Exp

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Pan, Yuetao, Danfeng Liu, Liguo Wang, Jón Atli Benediktsson, and Shishuai Xing. "A Pan-Sharpening Method with Beta-Divergence Non-Negative Matrix Factorization in Non-Subsampled Shear Transform Domain." Remote Sensing 14, no. 12 (2022): 2921. http://dx.doi.org/10.3390/rs14122921.

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In order to combine the spectral information of the multispectral (MS) image and the spatial information of the panchromatic (PAN) image, a pan-sharpening method based on β-divergence Non-negative Matrix Factorization (NMF) in the Non-Subsampled Shearlet Transform (NSST) domain is proposed. Firstly, we improve the traditional contrast calculation method to build the weighted local contrast measure (WLCM) method. Each band of the MS image is fused by a WLCM-based adaptive weighted averaging rule to obtain the intensity component I. Secondly, an image matting model is introduced to retain the sp

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Liu, Yunan, Shanshan Zhang, Chunpeng Wang, and Jie Xu. "Single image super-resolution via hybrid resolution NSST prediction." Computer Vision and Image Understanding 207 (June 2021): 103202. http://dx.doi.org/10.1016/j.cviu.2021.103202.

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CHEN Guang-qiu, 陈广秋, 高印寒 GAO Yin-han, 才华 CAI Hua, 刘广文 LIU Guang-wen, and 段云鹏 DUAN Yun-peng. "Image fusion algorithm based on local NSST and PCNN." Chinese Journal of Liquid Crystals and Displays 30, no. 4 (2015): 701–12. http://dx.doi.org/10.3788/yjyxs20153004.0701.

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WANG, Xianghai, Chuanming SONG, Yihuan ZHU, Xiaoyang ZHAO, and Ruoxi SONG. "Image NSST-HMT model with associated multi-state coefficients." SCIENTIA SINICA Informationis 49, no. 6 (2019): 708–25. http://dx.doi.org/10.1360/n112017-00293.

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Kong, Weiwei. "Technique for image fusion based on NSST domain INMF." Optik 125, no. 11 (2014): 2716–22. http://dx.doi.org/10.1016/j.ijleo.2013.11.025.

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Li, Xinchen, Dan Jing, Yachao Li, et al. "Multi-Band and Polarization SAR Images Colorization Fusion." Remote Sensing 14, no. 16 (2022): 4022. http://dx.doi.org/10.3390/rs14164022.

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The image fusion of multi-band and multi-polarization synthetic aperture radar (SAR) images can improve the efficiency of band and polarization information processing. In this paper, we introduce a fusion method that simultaneously fuses multi-band and polarization SAR images. In the method, we first use non-subsampled shearlet transform (NSST) to fuse multi-band and polarization SAR images. The sub-band images decomposed from the NSST are fused by the coefficient of variation (CV) and phase consistency (PC) weighted fusion rules. Subsequently, we extract the band and polarization difference i

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He, Xingkun, Can Wang, Rongyao Zheng, and Xiwen Li. "GPR Image Noise Removal Using Grey Wolf Optimisation in the NSST Domain." Remote Sensing 13, no. 21 (2021): 4416. http://dx.doi.org/10.3390/rs13214416.

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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

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Ding, Huijie, and Arthur K. L. Lin. "Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data." Mathematical Problems in Engineering 2020 (November 19, 2020): 1–6. http://dx.doi.org/10.1155/2020/8885887.

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Considering the defaults in synthetic aperture radar (SAR) image feature extraction, an SAR target recognition method based on non-subsampled Shearlet transform (NSST) was proposed with application to target recognition. NSST was used to decompose an SAR image into multilevel representations. These representations were translation-invariant, and they could well reflect the dominant and detailed properties of the target. During the machine learning classification stage, the joint sparse representation was employed to jointly represent the multilevel representations. The joint sparse representat

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Katta, Swapna, Prabhishek Singh, Deepak Garg, and Manoj Diwakar. "A Hybrid Approach for CT Image Noise Reduction Combining Method Noise-CNN and Shearlet Transform." Biomedical and Pharmacology Journal 17, no. 3 (2024): 1875–98. http://dx.doi.org/10.13005/bpj/2991.

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The presence of gaussian noise commonly weakens the diagnostic precision of low-dose CT imaging. A novel CT image denoising technique that integrates the non-subsampled shearlet transform (NSST) with Bayesian thresholding, and incorporates a modern method noise Deep Convolutional neural network (DCNN) based post-processing operation on denoised images to strengthen low-dose CT imaging quality. The hybrid method commences with NSST and Bayesian thresholding to mitigate the initial noise while preserving crucial image features, such as corners and edges. The novel aspect of the proposed approach

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Wang, Xiaobei, Rencan Nie, and Xiaopeng Guo. "Medical image fusion based on variational and nonlinear structure tensor." MATEC Web of Conferences 189 (2018): 10021. http://dx.doi.org/10.1051/matecconf/201818910021.

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Medical image fusion plays an important role in detection and treatment of disease. Although numerous medical image fusion methods have been proposed, most of them decrease the contrast and lose the image information. In this paper, a novel MRI and CT image fusion method is proposed combining rolling guidance filter, structure tensor, and nonsubsampled shearlet transform (NSST). First, the rolling guidance filter and the sum-modified laplacian (SML) operator are introduced in the algorithm to construct the weight maps in non-linear domain, then the fused gradient is firstly obtained by a new w

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S, Yallamandaiah, and N. Purnachand. "Convolutional neural network-based face recognition using non-subsampled shearlet transform and histogram of local feature descriptors." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (2021): 1079. http://dx.doi.org/10.11591/ijai.v10.i4.pp1079-1090.

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<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

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Wang, Xianghai, Zhenhua Mu, Shifu Bai, Yining Feng, and Ruoxi Song. "MS-Pansharpening Algorithm Based on Dual Constraint Guided Filtering." Remote Sensing 14, no. 19 (2022): 4867. http://dx.doi.org/10.3390/rs14194867.

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The difference and complementarity of spatial and spectral information between multispectral (MS) image and panchromatic (PAN) image have laid the foundation for the fusion of the two types of images. In recent years, MS and PAN image fusion (also known as MS-Pansharpening) has gained attention as an important research area in remote sensing (RS) image processing. This paper proposes an MS-Pansharpening algorithm based on dual constraint Guided Filtering in the nonsubsampled shearlet transform (NSST) domain. The innovation is threefold. First, the dual constraint guided image filtering (DCGIF)

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Qiu, Chenhui, Yuanyuan Wang, Huan Zhang, and Shunren Xia. "Image Fusion of CT and MR with Sparse Representation in NSST Domain." Computational and Mathematical Methods in Medicine 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9308745.

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Multimodal image fusion techniques can integrate the information from different medical images to get an informative image that is more suitable for joint diagnosis, preoperative planning, intraoperative guidance, and interventional treatment. Fusing images of CT and different MR modalities are studied in this paper. Firstly, the CT and MR images are both transformed to nonsubsampled shearlet transform (NSST) domain. So the low-frequency components and high-frequency components are obtained. Then the high-frequency components are merged using the absolute-maximum rule, while the low-frequency

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Zhao, Cheng, and Yongdong Huang. "Infrared and visible image fusion method based on rolling guidance filter and NSST." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 06 (2019): 1950045. http://dx.doi.org/10.1142/s0219691319500450.

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The rolling guidance filtering (RGF) has a good characteristic which can smooth texture and preserve the edges, and non-subsampled shearlet transform (NSST) has the features of translation invariance and direction selection based on which a new infrared and visible image fusion method is proposed. Firstly, the rolling guidance filter is used to decompose infrared and visible images into the base and detail layers. Then, the NSST is utilized on the base layer to get the high-frequency coefficients and low-frequency coefficients. The fusion of low-frequency coefficients uses visual saliency map

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Yu, Minghao, Xiangbo Gong, and Xiaojie Wan. "Seismic Coherent Noise Removal of Source Array in the NSST Domain." Applied Sciences 12, no. 21 (2022): 10846. http://dx.doi.org/10.3390/app122110846.

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The technique of the source array based on the vibroseis can provide the strong energy of a seismic wave field, which better meets the need for seismic exploration. The seismic coherent noise reduces the signal-to-noise ratio (SNR) of the source array seismic data and affects the seismic data processing. The traditional coherent noise removal methods often cause some damage to the effective signal while suppressing coherent noise or cannot suppress the interference wave effectively at all. Based on the multi-scale and multi-direction properties of the non-subsampled Shearlet transform (NSST) a

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Yan, Peizhou, Jiancheng Zou, Zhengzheng Li, and Xin Yang. "Infrared and Visible Image Fusion Based on NSST and RDN." Intelligent Automation & Soft Computing 28, no. 1 (2021): 213–25. http://dx.doi.org/10.32604/iasc.2021.016201.

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