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Journal articles on the topic 'Multi scale methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 December 2024

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1

Liu, Wing Kam, Su Hao, Ted Belytschko, Shaofan Li, and Chin Tang Chang. "Multi-scale methods." International Journal for Numerical Methods in Engineering 47, no. 7 (March 10, 2000): 1343–61. http://dx.doi.org/10.1002/(sici)1097-0207(20000310)47:7<1343::aid-nme828>3.0.co;2-w.

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2

de Borst, R. "Multi-Scale Methods and Evolving Discontinuities." Computational Technology Reviews 1 (September 14, 2010): 1–28. http://dx.doi.org/10.4203/ctr.1.1.

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3

Zhou, Xiaoxiang, and Leilei Chen. "Review on Multi-scale Simulation Methods." IOP Conference Series: Materials Science and Engineering 394 (August 7, 2018): 032139. http://dx.doi.org/10.1088/1757-899x/394/3/032139.

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4

Greve, L., and S. Vlachoutsis. "Multi-scale and multi-model methods for efficient crash simulation." International Journal of Crashworthiness 12, no. 4 (October 3, 2007): 437–48. http://dx.doi.org/10.1080/13588260701483425.

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Bardi, Istvan, Kezhong Zhao, Rickard Petersson, John Silvestro, and Nancy Lambert. "Multi-domain multi-scale problems in high frequency finite element methods." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 32, no. 5 (September 9, 2013): 1471–83. http://dx.doi.org/10.1108/compel-04-2013-0123.

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Mylonakis, A. G., M. Varvayanni, N. Catsaros, P. Savva, and D. G. E. Grigoriadis. "Multi-physics and multi-scale methods used in nuclear reactor analysis." Annals of Nuclear Energy 72 (October 2014): 104–19. http://dx.doi.org/10.1016/j.anucene.2014.05.002.

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7

Pereira, Drew Joseph, Taylor R. Garrick, and John W. Weidner. "Improvements to Multi-Scale, Mechano-Electrochemical Modeling Methods." ECS Meeting Abstracts MA2020-02, no. 68 (November 23, 2020): 3508. http://dx.doi.org/10.1149/ma2020-02683508mtgabs.

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8

Heinz, Stefan. "Stochastic Multi-Scale Methods for Turbulent Flow Simulations." PAMM 6, no. 1 (December 2006): 669–70. http://dx.doi.org/10.1002/pamm.200610315.

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9

Avramova, Maria, Agustin Abarca, Jason Hou, and Kostadin Ivanov. "Innovations in Multi-Physics Methods Development, Validation, and Uncertainty Quantification." Journal of Nuclear Engineering 2, no. 1 (March 7, 2021): 44–56. http://dx.doi.org/10.3390/jne2010005.

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This paper provides a review of current and upcoming innovations in development, validation, and uncertainty quantification of nuclear reactor multi-physics simulation methods. Multi-physics modelling and simulations (M&S) provide more accurate and realistic predictions of the nuclear reactors behavior including local safety parameters. Multi-physics M&S tools can be subdivided in two groups: traditional multi-physics M&S on assembly/channel spatial scale (currently used in industry and regulation), and novel high-fidelity multi-physics M&S on pin (sub-pin)/sub-channel spatial scale. The current trends in reactor design and safety analysis are towards further development, verification, and validation of multi-physics multi-scale M&S combined with uncertainty quantification and propagation. Approaches currently applied for validation of the traditional multi-physics M&S are summarized and illustrated using established Nuclear Energy Agency/Organization for Economic Cooperation and Development (NEA/OECD) multi-physics benchmarks. Novel high-fidelity multi-physics M&S allow for insights crucial to resolve industry challenge and high impact problems previously impossible with the traditional tools. Challenges in validation of novel multi-physics M&S are discussed along with the needs for developing validation benchmarks based on experimental data. Due to their complexity, the novel multi-physics codes are still computationally expensive for routine applications. This fact motivates the use of high-fidelity novel models and codes to inform the low-fidelity traditional models and codes, leading to improved traditional multi-physics M&S. The uncertainty quantification and propagation across different scales (multi-scale) and multi-physics phenomena are demonstrated using the OECD/NEA Light Water Reactor Uncertainty Analysis in Modelling benchmark framework. Finally, the increasing role of data science and analytics techniques in development and validation of multi-physics M&S is summarized.

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Allard, William K., Guangliang Chen, and Mauro Maggioni. "Multi-scale geometric methods for data sets II: Geometric Multi-Resolution Analysis." Applied and Computational Harmonic Analysis 32, no. 3 (May 2012): 435–62. http://dx.doi.org/10.1016/j.acha.2011.08.001.

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Pang, Z., X. Qin, W. Jiang, J. Fu, K. Yang, J. Lu, W. Qu, L. Li, and X. Li. "THE REVIEW OF SOIL MOISTURE MULTI-SCALE VERIFICATION METHODS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 395–99. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-395-2020.

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Abstract. Soil moisture is an important physical parameter to investigate water circulation, while it is difficult to be measured with spatiotemporal consistency. During the past several decades, a larger number of soil moisture verification methods were proposed, however, the review of soil moisture verification method from multi-scale perspective is still lacking. This paper investigates the verification method of soil moisture from three scale, such as point-scale, regional scale and remote sensing data verification. The prospect of soil moisture verification is proposed to serve retrieval algorithm validation.

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Duncan, Jacob P., Rachel N. Rozum, James A. Powell, and Karin M. Kettenring. "Multi-scale methods predict invasion speeds in variable landscapes." Theoretical Ecology 10, no. 3 (February 27, 2017): 287–303. http://dx.doi.org/10.1007/s12080-017-0329-0.

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13

Engquist, Björn, Henrik Holst, and Olof Runborg. "Multi-scale methods for wave propagation in heterogeneous media." Communications in Mathematical Sciences 9, no. 1 (2011): 33–56. http://dx.doi.org/10.4310/cms.2011.v9.n1.a2.

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14

Kubař, Tomáš, Rafael Gutiérrez, Ulrich Kleinekathöfer, Gianaurelio Cuniberti, and Marcus Elstner. "Modeling charge transport in DNA using multi-scale methods." physica status solidi (b) 250, no. 11 (August 9, 2013): 2277–87. http://dx.doi.org/10.1002/pssb.201349148.

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15

Abdulle, Assyr. "Solving Multi-Scale problems with heterogeneous finite difference methods." PAMM 3, no. 1 (December 2003): 575–76. http://dx.doi.org/10.1002/pamm.200310555.

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16

Brandner, Astrid F., Stepan Timr, Simone Melchionna, Philippe Derreumaux, Marc Baaden, and Fabio Sterpone. "Advancing Multi-Scale Simulation Methods for Biological Membrane Systems." Biophysical Journal 116, no. 3 (February 2019): 373a—374a. http://dx.doi.org/10.1016/j.bpj.2018.11.2031.

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17

Berg, Peter, Thomas Bosshard, Wei Yang, and Klaus Zimmermann. "MIdASv0.2.1 – MultI-scale bias AdjuStment." Geoscientific Model Development 15, no. 15 (August 5, 2022): 6165–80. http://dx.doi.org/10.5194/gmd-15-6165-2022.

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Abstract. Bias adjustment is the practice of statistically transforming climate model data in order to reduce systematic deviations from a reference data set, typically some sort of observations. There are numerous proposed methodologies to perform the adjustments – ranging from simple scaling approaches to advanced multi-variate distribution-based mapping. In practice, the actual bias adjustment method is a small step in the application, and most of the processing handles reading, writing, and linking different data sets. These practical processing steps become especially heavy with increasing model domain size and resolution in both time and space. Here, we present a new implementation platform for bias adjustment, which we call MIdAS (MultI-scale bias AdjuStment). MIdAS is a modern code implementation that supports features such as modern Python libraries that allow efficient processing of large data sets at computing clusters, state-of-the-art bias adjustment methods based on quantile mapping, and “day-of-year-based” adjustments to avoid artificial discontinuities, and it also introduces cascade adjustment in time and space. The MIdAS platform has been set up such that it will continually support development of methods aimed towards higher-resolution climate model data, explicitly targeting cases where there is a scale mismatch between data sets. The paper presents a comparison of different quantile-mapping-based bias adjustment methods and the subsequently chosen code implementation for MIdAS. A current recommended setup of the MIdAS bias adjustment is presented and evaluated in a pseudo-reference setup for regions around the world. Special focus is put on preservation of trends in future climate projections, and it is shown that the cascade adjustments perform better than the standard quantile mapping implementations and are often similar to methods that explicitly preserve trends.

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Podsiadlo, P., and G. W. Stachowiak. "Multi-scale representation of tribological surfaces." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 216, no. 6 (June 1, 2002): 463–79. http://dx.doi.org/10.1243/135065002762355361.

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Many numerical surface topography analysis methods exist today. However, even for the moderately complicated topography of a tribological surface these methods can provide only limited information. The reason is that tribological surfaces often exhibit a non-stationary and multi-scale nature while the numerical methods currently used work well with surface data exhibiting a stationary random process and provide surface descriptors closely related to a scale at which surface data were acquired. The suitability of different methods, including Fourier transform, windowed Fourier transform, Cohen's class distributions (especially the Wigner-Ville distribution), wavelet transform, fractal methods and a hybrid fractal-wavelet method, for the analysis of tribological surface topographies is investigated in this paper. The method best suited to this purpose has been selected.

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19

Wenhan, Li, Lu Kailiang, Li He, Cui Hongliang, and Li Xiu. "New multi-resolution and multi-scale electromagnetic detection methods for urban underground spaces." Journal of Applied Geophysics 159 (December 2018): 742–53. http://dx.doi.org/10.1016/j.jappgeo.2018.09.034.

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20

Jamal, M. Hasan, Arun Prakash, and Milind Kulkarni. "Exploiting semantics of temporal multi-scale methods to optimize multi-level mesh partitioning." International Journal for Numerical Methods in Engineering 112, no. 1 (February 22, 2017): 58–85. http://dx.doi.org/10.1002/nme.5506.

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21

Zupan, Nina, and Jože Korelc. "Sensitivity analysis based multi-scale methods of coupled path-dependent problems." Computational Mechanics 65, no. 1 (September 18, 2019): 229–48. http://dx.doi.org/10.1007/s00466-019-01762-8.

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Abstract In the paper, a generalized essential boundary condition sensitivity analysis based implementation of $$\text {FE}^2$$FE2 and mesh-in-element (MIEL) multi-scale methods is derived as an alternative to standard implementations of multi-scale analysis, where the calculation of Schur complement of the microscopic tangent matrix is needed for bridging the scales. The paper presents a unified approach to the development of an arbitrary MIEL or $$\text {FE}^2$$FE2 computational scheme for an arbitrary path-dependent material model. Implementation is based on efficient first and second order analytical sensitivity analysis, for which automatic-differentiation-based formulation of essential boundary condition sensitivity analysis is derived. A fully consistently linearized two-level path-following algorithm is introduced as a solution algorithm for the multi-scale modeling. Sensitivity analysis allows each macro step to be followed by an arbitrary number of micro substeps while retaining quadratic convergence of the overall solution algorithm.

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22

Wang, Qing Hua, Shi En Ri, Hiroshi Tsuda, Satoshi Kishimoto, Yoshihisa Tanaka, and Yutaka Kagawa. "Fabrication of Multi-Scale Grid Patterns as Deformation Carriers in Optical Methods." Applied Mechanics and Materials 782 (August 2015): 271–77. http://dx.doi.org/10.4028/www.scientific.net/amm.782.271.

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Multi-scale grid is an essential deformation carrier in optical methods for multi-scale deformation measurement. In this study, several new-type multi-scale grids were designed and fabricated by electron beam lithography. Each pattern includes several periodically distributed dots with the same spacing but different sizes. As a consequence, the grayscale of the whole grid pattern periodically changes. The peak parts of the grayscale generate a secondary grid, i.e., the large-scale grid. The ratio of the large-scale grid pitch to the small-scale grid pitch can be easily adjusted according to the requirement. The natural integration between the small-scale grid and the large-scale grid works well in eliminating the mutual disturbance between the different-scale grids. Besides, this type of grid has a very high success rate in fabrication owing to the small differences in size between the big dots and the small dots. The proposed multi-scale grid pattern is expected to serve as the deformation carrier in moiré methods and geometric phase analysis for multi-scale deformation measurement.

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23

Jian, Lihua, Shaowu Wu, Lihui Chen, Gemine Vivone, Rakiba Rayhana, and Di Zhang. "Multi-Scale and Multi-Stream Fusion Network for Pansharpening." Remote Sensing 15, no. 6 (March 20, 2023): 1666. http://dx.doi.org/10.3390/rs15061666.

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Pansharpening refers to the use of a panchromatic image to improve the spatial resolution of a multi-spectral image while preserving spectral signatures. However, existing pansharpening methods are still unsatisfactory at balancing the trade-off between spatial enhancement and spectral fidelity. In this paper, a multi-scale and multi-stream fusion network (named MMFN) that leverages the multi-scale information of the source images is proposed. The proposed architecture is simple, yet effective, and can fully extract various spatial/spectral features at different levels. A multi-stage reconstruction loss was adopted to recover the pansharpened images in each multi-stream fusion block, which facilitates and stabilizes the training process. The qualitative and quantitative assessment on three real remote sensing datasets (i.e., QuickBird, Pléiades, and WorldView-2) demonstrates that the proposed approach outperforms state-of-the-art methods.

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24

Ban, Yuseok, and Kyungjae Lee. "Multi-Scale Ensemble Learning for Thermal Image Enhancement." Applied Sciences 11, no. 6 (March 22, 2021): 2810. http://dx.doi.org/10.3390/app11062810.

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In this study, we propose a multi-scale ensemble learning method for thermal image enhancement in different image scale conditions based on convolutional neural networks. Incorporating the multiple scales of thermal images has been a tricky task so that methods have been individually trained and evaluated for each scale. However, this leads to the limitation that a network properly operates on a specific scale. To address this issue, a novel parallel architecture leveraging the confidence maps of multiple scales have been introduced to train a network that operates well in varying scale conditions. The experimental results show that our proposed method outperforms the conventional thermal image enhancement methods. The evaluation is presented both quantitatively and qualitatively.

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25

Kong, Lu, Ying Zi Song, and Da Ming You. "Research on Data Assimilation Methods of Multi-Space-Time Meteroligical and Hydrological Data." Advanced Materials Research 659 (January 2013): 118–22. http://dx.doi.org/10.4028/www.scientific.net/amr.659.118.

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Meteorological and hydrological data has the feature of multi-semantic space, multi-space-time, multi-scale and it has a diversed means to be acquired and storaged, which brings the diversity of multi-origin characteristics. This paper will adopt the method of data assimilation to study a variety of data models with different scales and the key technology the grid resolution spatial data integration in order to establish a meteorological and hydrological model of multi-source spatial data assimilation.

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26

Looney, David, Apit Hemakom, and Danilo P. Mandic. "Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471, no. 2173 (January 2015): 20140709. http://dx.doi.org/10.1098/rspa.2014.0709.

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A novel multi-scale approach for quantifying both inter- and intra-component dependence of a complex system is introduced. This is achieved using empirical mode decomposition (EMD), which, unlike conventional scale-estimation methods, obtains a set of scales reflecting the underlying oscillations at the intrinsic scale level. This enables the data-driven operation of several standard data-association measures (intrinsic correlation, intrinsic sample entropy (SE), intrinsic phase synchrony) and, at the same time, preserves the physical meaning of the analysis. The utility of multi-variate extensions of EMD is highlighted, both in terms of robust scale alignment between system components, a pre-requisite for inter-component measures, and in the estimation of feature relevance. We also illuminate that the properties of EMD scales can be used to decouple amplitude and phase information, a necessary step in order to accurately quantify signal dynamics through correlation and SE analysis which are otherwise not possible. Finally, the proposed multi-scale framework is applied to detect directionality, and higher order features such as coupling and regularity, in both synthetic and biological systems.

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27

Agarwal, Sugandha, O. P. Singh, Deepak Nagaria, Anil Kumar Tiwari, and Shikha Singh. "Image Quality Improvement Using Shift Variant and Shift Invariant Based Wavelet Transform Methods." International Journal of Multimedia Data Engineering and Management 8, no. 3 (July 2017): 42–54. http://dx.doi.org/10.4018/ijmdem.2017070103.

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The concept of Multi-Scale Transform (MST) based image de-noising methods is incorporated in this paper. The shortcomings of Fourier transform based methods have been improved using multi-scale transform, which help in providing the local information of non-stationary image at different scales which is indispensable for de-noising. Multi-scale transform based image de-noising methods comprises of Discrete Wavelet Transform (DWT), and Stationary Wavelet Transform (SWT). Both DWT and SWT techniques are incorporated for the de-noising of standard images. Further, the performance comparison has been noted by using well defined metrics, such as, Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) and Computation Time (CT). The result shows that SWT technique gives better performance as compared to DWT based de-noising technique in terms of both analytical and visual evaluation.

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28

Nichols, James D., Larissa L. Bailey, Allan F. O’Connell Jr., Neil W. Talancy, Evan H. Campbell Grant, Andrew T. Gilbert, Elizabeth M. Annand, Thomas P. Husband, and James E. Hines. "Multi-scale occupancy estimation and modelling using multiple detection methods." Journal of Applied Ecology 45, no. 5 (October 2008): 1321–29. http://dx.doi.org/10.1111/j.1365-2664.2008.01509.x.

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29

Xie, Shengkun, and Sridhar Krishnan. "Signal classification via multi-scale PCA and empirical classification methods." International Journal of Mechatronics and Automation 1, no. 3/4 (2011): 213. http://dx.doi.org/10.1504/ijma.2011.045253.

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30

Ge, Bao, Yin Tian, Xintao Hu, Hanbo Chen, Dajiang Zhu, Tuo Zhang, Junwei Han, Lei Guo, and Tianming Liu. "Construction of Multi-Scale Consistent Brain Networks: Methods and Applications." PLOS ONE 10, no. 4 (April 13, 2015): e0118175. http://dx.doi.org/10.1371/journal.pone.0118175.

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31

Pulch, R. "Finite difference methods for multi time scale differential algebraic equations." ZAMM 83, no. 9 (September 1, 2003): 571–83. http://dx.doi.org/10.1002/zamm.200310042.

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32

Haug, Jonas, and Ray Treinen. "Multi-scale spectral methods for bounded radially symmetric capillary surfaces." ETNA - Electronic Transactions on Numerical Analysis 60 (2024): 20–39. http://dx.doi.org/10.1553/etna_vol60s20.

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33

Baldacci, R., M. A. Boschetti, N. Christofides, and S. Christofides. "Exact methods for large-scale multi-period financial planning problems." Computational Management Science 6, no. 3 (January 31, 2007): 281–306. http://dx.doi.org/10.1007/s10287-006-0037-5.

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34

Cai, Borui, Guangyan Huang, Yong Xiang, Maia Angelova, Limin Guo, and Chi-Hung Chi. "Multi-Scale Shapelets Discovery for Time-Series Classification." International Journal of Information Technology & Decision Making 19, no. 03 (May 2020): 721–39. http://dx.doi.org/10.1142/s0219622020500133.

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Shapelets are subsequences of time-series that represent local patterns and can improve the accuracy and the interpretability of time-series classification. The major task of time-series classification using shapelets is to discover high quality shapelets. However, this is challenging since local patterns may have various scales/lengths rather than a unified scale. In this paper, we resolve this problem by discovering shapelets with multiple scales. We propose a novel Multi-Scale Shapelet Discovery (MSSD) algorithm to discover expressive multi-scale shapelets by extending initial single-scale shapelets (i.e., shapelets with a unified scale). MSSD adopts a bi-directional extension process and is robust to extend single-shapelets obtained by different methods. A supervised shapelet quality measurement is further developed to qualify the extension of shapelets. Comprehensive experiments conducted on 25 UCR time-series datasets show that multi-scale shapelets discovered by MSSD improve classification accuracy by around 10% (in average), compared with single-scale shapelets discovered by counterpart methods.

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35

Michel, A., H. Stang, M. Lepech, and M. R. Geiker. "Multi-Physics and Multi-Scale Deterioration Modelling of Reinforced Concrete." Key Engineering Materials 665 (September 2015): 13–16. http://dx.doi.org/10.4028/www.scientific.net/kem.665.13.

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Deterioration of reinforced concrete infrastructure such as bridges, tunnels, and buildings represents one of the major challenges currently facing developed countries. While engineering tools and methods for structural modelling and design of new reinforced concrete infrastructure are mature, methods and tools for modelling decades-long deterioration and maintenance are much less developed. In this paper, a multi-physics and multi-scale modelling approach for structural deterioration of reinforced concrete components due to reinforcement corrosion is presented. The multi-disciplinary modelling approach includes physical, chemical, electrochemical, and fracture mechanical processes at the material and meso-scale, which are further coupled with mechanical deterioration processes at the structural scale.

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36

De Cooman, Bruno C., H. K. D. H. Bhadeshia, and Frédéric Barlat. "Advanced Steel Design by Multi-Scale Modeling." Materials Science Forum 654-656 (June 2010): 41–46. http://dx.doi.org/10.4028/www.scientific.net/msf.654-656.41.

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The present contribution highlights the approach to multi-scale steel design used at the Graduate Institute of Ferrous Technology (GIFT). Multi-scale modeling combining ab-initio methods, molecular dynamics, crystal plasticity modeling etc. enables GIFT researchers to gain a better fundamental understanding of phase and lattice stability, magnetic properties and basic mechanical constants. In addition, these methods allow for the reliable determination of critical material parameters. The opportunities for the development of new steel grade is thereby greatly enhanced and, when these new materials-oriented methods are combined with the more traditional engineering modeling methods, the challenges related to the large scale production of new steel grades can also be addressed.

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Li, Simin, Xueyu Zhu, and Jie Bao. "Hierarchical Multi-Scale Convolutional Neural Networks for Hyperspectral Image Classification." Sensors 19, no. 7 (April 10, 2019): 1714. http://dx.doi.org/10.3390/s19071714.

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Deep learning models combining spectral and spatial features have been proven to be effective for hyperspectral image (HSI) classification. However, most spatial feature integration methods only consider a single input spatial scale regardless of various shapes and sizes of objects over the image plane, leading to missing scale-dependent information. In this paper, we propose a hierarchical multi-scale convolutional neural networks (CNNs) with auxiliary classifiers (HMCNN-AC) to learn hierarchical multi-scale spectral–spatial features for HSI classification. First, to better exploit the spatial information, multi-scale image patches for each pixel are generated at different spatial scales. These multi-scale patches are all centered at the same central spectrum but with shrunken spatial scales. Then, we apply multi-scale CNNs to extract spectral–spatial features from each scale patch. The obtained multi-scale convolutional features are considered as structured sequential data with spectral–spatial dependency, and a bidirectional LSTM is proposed to capture the correlation and extract a hierarchical representation for each pixel. To better train the whole network, weighted auxiliary classifiers are employed for the multi-scale CNNs and optimized together with the main loss function. Experimental results on three public HSI datasets demonstrate the superiority of our proposed framework over some state-of-the-art methods.

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38

Ma, Xiaole, Zhihai Wang, Shaohai Hu, and Shichao Kan. "Multi-Focus Image Fusion Based on Multi-Scale Generative Adversarial Network." Entropy 24, no. 5 (April 21, 2022): 582. http://dx.doi.org/10.3390/e24050582.

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The methods based on the convolutional neural network have demonstrated its powerful information integration ability in image fusion. However, most of the existing methods based on neural networks are only applied to a part of the fusion process. In this paper, an end-to-end multi-focus image fusion method based on a multi-scale generative adversarial network (MsGAN) is proposed that makes full use of image features by a combination of multi-scale decomposition with a convolutional neural network. Extensive qualitative and quantitative experiments on the synthetic and Lytro datasets demonstrated the effectiveness and superiority of the proposed MsGAN compared to the state-of-the-art multi-focus image fusion methods.

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Kim, Minseong, and Hyun-Chul Choi. "Total Style Transfer with a Single Feed-Forward Network." Sensors 22, no. 12 (June 18, 2022): 4612. http://dx.doi.org/10.3390/s22124612.

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The development of recent image style transfer methods allows the quick transformation of an input content image into an arbitrary style. However, these methods have a limitation that the scale-across style pattern of a style image cannot be fully transferred into a content image. In this paper, we propose a new style transfer method, named total style transfer, that resolves this limitation by utilizing intra/inter-scale statistics of multi-scaled feature maps without losing the merits of the existing methods. First, we use a more general feature transform layer that employs intra/inter-scale statistics of multi-scaled feature maps and transforms the multi-scaled style of a content image into that of a style image. Secondly, we generate a multi-scaled stylized image by using only a single decoder network with skip-connections, in which multi-scaled features are merged. Finally, we optimize the style loss for the decoder network in the intra/inter-scale statistics of image style. Our improved total style transfer can generate a stylized image with a scale-across style pattern from a pair of content and style images in one forwarding pass. Our method achieved less memory consumption and faster feed-forwarding speed compared with the recent cascade scheme and the lowest style loss among the recent style transfer methods.

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40

Fei Qi, Fei Qi, and Chen-Qing Wang Fei Qi. "An End-to-End Multi-Scale Conditional Generative Adversarial Network for Image Deblurring." 電腦學刊 34, no. 3 (June 2023): 237–50. http://dx.doi.org/10.53106/199115992023063403017.

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<p>For image deblurring, multi-scale approaches have been widely used as deep learning methods recently. In this paper, a novel multi-scale conditional generative adversarial network (CGAN) is proposed to make full use of image features, which outperforms most state-of-the-art methods. We define a generator network and a discriminator network. First of all, we use the multi-scale residual modules proposed in this paper as main feature extraction blocks, and add skip connections to extract multi-scale image features at a finer granularity in the generator network. Secondly, we construct PatchGAN as the discriminator network to enhance the local feature extraction capability. In addition, we combine the adversarial loss based on Wasserstein GAN with gradient penalty (WGAN-GP) theory with the content loss defined by perceptual loss as the total loss function, which is conducive to improving the consistency between the generated images and the ground-truth sharp images in content. The experimental results show that the method in this paper outperforms the state-of-the-art methods in visualization and quantitative results.</p> <p> </p>

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Dogra, Ayush, Bhawna Goyal, and Sunil Agrawal. "From Multi-Scale Decomposition to Non-Multi-Scale Decomposition Methods: A Comprehensive Survey of Image Fusion Techniques and Its Applications." IEEE Access 5 (2017): 16040–67. http://dx.doi.org/10.1109/access.2017.2735865.

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Wang, Fang, Xingqian Du, Weiguang Zhang, Liang Nie, Hu Wang, Shun Zhou, and Jun Ma. "Remote Sensing LiDAR and Hyperspectral Classification with Multi-Scale Graph Encoder–Decoder Network." Remote Sensing 16, no. 20 (October 21, 2024): 3912. http://dx.doi.org/10.3390/rs16203912.

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The rapid development of sensor technology has made multi-modal remote sensing data valuable for land cover classification due to its diverse and complementary information. Many feature extraction methods for multi-modal data, combining light detection and ranging (LiDAR) and hyperspectral imaging (HSI), have recognized the importance of incorporating multiple spatial scales. However, effectively capturing both long-range global correlations and short-range local features simultaneously on different scales remains a challenge, particularly in large-scale, complex ground scenes. To address this limitation, we propose a multi-scale graph encoder–decoder network (MGEN) for multi-modal data classification. The MGEN adopts a graph model that maintains global sample correlations to fuse multi-scale features, enabling simultaneous extraction of local and global information. The graph encoder maps multi-modal data from different scales to the graph space and completes feature extraction in the graph space. The graph decoder maps the features of multiple scales back to the original data space and completes multi-scale feature fusion and classification. Experimental results on three HSI-LiDAR datasets demonstrate that the proposed MGEN achieves considerable classification accuracies and outperforms state-of-the-art methods.

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Lee, Si-Hun, Yongse Kim, DuHyun Gong, HyunShig Joo, Haeseong Cho, Haedong Kim, and SangJoon Shin. "Fast and Novel Computational Methods for Multi-scale and Multi-physics: FETI and POD-ROM." Multiscale Science and Engineering 2, no. 2-3 (September 2020): 189–97. http://dx.doi.org/10.1007/s42493-020-00048-z.

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Hu, Rongyao, Zhenyun Deng, and Xiaofeng Zhu. "Multi-scale Graph Fusion for Co-saliency Detection." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7789–96. http://dx.doi.org/10.1609/aaai.v35i9.16951.

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The key challenge of co-saliency detection is to extract discriminative features to distinguish the common salient foregrounds from backgrounds in a group of relevant images. In this paper, we propose a new co-saliency detection framework which includes two strategies to improve the discriminative ability of the features. Specifically, on one hand, we segment each image to semantic superpixel clusters as well as generate different scales/sizes of images for each input image by the VGG-16 model. Different scales capture different patterns of the images. As a result, multi-scale images can capture various patterns among all images by many kinds of perspectives. Second, we propose a new method of Graph Convolutional Network (GCN) to fine-tune the multi-scale features, aiming at capturing the common information among the features from all scales and the private or complementary information for the feature of each scale. Moreover, the proposed GCN method jointly conducts multi-scale feature fine-tune, graph learning, and feature learning in a unified framework. We evaluated our method on three benchmark data sets, compared to state-of-the-art co-saliency detection methods. Experimental results showed that our method outperformed all comparison methods in terms of different evaluation metrics.

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P, Sangeeta. "Aquavision Alchemy Illuminating Underwater Imagery Through Medium Transmission." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 10, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30515.

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Restoring underwater scenes often involves addressing interference caused by the underwater environment. Many existing methods overlook the scale-related characteristics inherent in these scenes. To tackle this, we propose a synergistic multi-scale detail refinement method for enhancing underwater scene details. This approach comprises multiple stages, starting with a low-degradation stage that enriches original images with multi-scale details using the Adaptive Selective Intrinsic Supervised Feature module. ASISF, through intrinsic supervision, precisely manages feature transmission across different degradation stages, refining multi-scale details while minimizing irrelevant information. Within the framework, we introduce the Bifocal Intrinsic-Context Attention Module, which efficiently utilizes multi-scale scene information by leveraging spatial contextual relationships. Throughout training, a multi-degradation loss function enhances the network's ability to extract information across various scales. The proposed method consistently outperforms state-of-the-art methods when evaluated against them. Key Words: Interference mitigation, Multi-scale detail refinement, Intrinsic supervision, Spatial contextual relationships.

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Lamberti, Luciano. "Advances in Multi-Scale Mechanical Characterization of Materials with Optical Methods." Materials 14, no. 23 (November 28, 2021): 7282. http://dx.doi.org/10.3390/ma14237282.

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The mechanical characterization of materials embraces many different aspects, such as, for example, (i) to assess materials’ constitutive behavior under static and dynamic conditions; (ii) to analyze material microstructure; (iii) to assess the level of damage developed in the material; (iv) to determine surface/interfacial properties; and (v) to optimize manufacturing processes in terms of process speed and reliability and obtain the highest quality of manufactured products [...]

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Coletti, Chiara, Alessandro Borghi, Roberto Cossio, Maria Chiara Dalconi, Giorgia Dalla Santa, Luca Peruzzo, Raffaele Sassi, Arianna Vettorello, and Antonio Galgaro. "A multi-scale methods comparison to provide granitoid rocks thermal conductivity." Construction and Building Materials 304 (October 2021): 124612. http://dx.doi.org/10.1016/j.conbuildmat.2021.124612.

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Ge, Bao, Yin Tian, Xintao Hu, Hanbo Chen, Dajiang Zhu, Tuo Zhang, Junwei Han, Lei Guo, and Tianming Liu. "Correction: Construction of Multi-Scale Consistent Brain Networks: Methods and Applications." PLOS ONE 10, no. 6 (June 3, 2015): e0130054. http://dx.doi.org/10.1371/journal.pone.0130054.

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Coelho, Pedro J., Nicolas Crouseilles, Pedro Pereira, and Maxime Roger. "Multi-scale methods for the solution of the radiative transfer equation." Journal of Quantitative Spectroscopy and Radiative Transfer 172 (March 2016): 36–49. http://dx.doi.org/10.1016/j.jqsrt.2015.10.001.

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Dimarco, Giacomo, and Lorenzo Pareschi. "Multi-scale control variate methods for uncertainty quantification in kinetic equations." Journal of Computational Physics 388 (July 2019): 63–89. http://dx.doi.org/10.1016/j.jcp.2019.03.002.

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