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Статті в журналах з теми "Image extrapolation"

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Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Zhang, Jie, and George A. McMechan. "Turning wave migration by horizontal extrapolation." GEOPHYSICS 62, no. 1 (January 1997): 291–97. http://dx.doi.org/10.1190/1.1444130.

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Conventional migration based on depth stepping extrapolation fails to migrate turning wave energy because of its inability to propagate energy with dips beyond 90°. A viable strategy for imaging turning waves is to use horizontal, rather than depth, extrapolation. This can be implemented by a 90° rotation of the extrapolator so that the data are extrapolated horizontally rather than vertically. In this geometry, the energy associated with turned rays consistently moves in the same direction as the extrapolation, and so only one pass is necessary to image turned reflections. The viability of this strategy is demonstrated with both synthetic and field poststack data that include turned reflections from salt flanks. Depth extrapolation images the near‐horizontal structure and horizontal extrapolation images the near‐vertical structure, and combining them gives a full image containing all dips.

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2

Spirik, Jan, and Jan Zatyik. "Image Extrapolation Using Sparse Methods." Communications - Scientific letters of the University of Zilina 15, no. 2A (July 31, 2013): 174–79. http://dx.doi.org/10.26552/com.c.2013.2a.174-179.

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3

Nguyen, Bao D., and George A. McMechan. "Five ways to avoid storing source wavefield snapshots in 2D elastic prestack reverse time migration." GEOPHYSICS 80, no. 1 (January 1, 2015): S1—S18. http://dx.doi.org/10.1190/geo2014-0014.1.

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Five alternative algorithms were evaluated to circumvent the excessive storage requirement imposed by saving source wavefield snapshots used for the crosscorrelation image condition in 2D prestack elastic reverse time migration. We compared the algorithms on the basis of their ability, either to accurately reconstruct (not save) the source wavefield or to use an alternate image condition so that neither saving nor reconstruction of full wavefields was involved. The comparisons were facilitated by using the same (velocity-stress) extrapolator in all the algorithms, and running them all on the same hardware. We assumed that there was enough memory in a node to do an extrapolation, and that all input data were stored on disk rather than residing in random-access memory. This should provide a fair and balanced comparison. Reconstruction of the source wavefield from boundary and/or initial values reduced the required storage to a very small fraction of that needed to store source wavefield snapshots for conventional crosscorrelation, at the cost of adding an additional source extrapolation. Reverse time checkpointing avoided recursive forward recomputation. Two nonreconstructive imaging conditions do not require full snapshot storage or an additional extrapolation. Time-binning the imaging criteria removed the need for image time searching or sorting. Numerical examples using elastic data from the Marmousi2 model showed that the quality of the elastic prestack PP and PS images produced by the cost-optimized alternative algorithms were (virtually) identical to the higher cost images produced by traditional crosscorrelation.

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4

Addlin, Shiney R., Kumar T. Saravana, and Mary M. Roslin. "Automatic Extrapolation of User Intention for Internet Image Search without Duplication." Applied Mechanics and Materials 573 (June 2014): 447–52. http://dx.doi.org/10.4028/www.scientific.net/amm.573.447.

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.Image search engines (e.g. Google Image Search, Bing Image Search) mostly depends on the given query surrounding text features. It increases complexity to interpret users search intention only by giving single query keywords and this leads to ambiguous and noisy search results. To solve the ambiguity in the image search, consider visual information along with the text features. In this approach user has to click a single search return image and the search results are re-ranked based on the similarity in visual and textual content. Our work is to capture user search intention by doing one-click image search has four steps. Adaptive weight categories are predefined to category the query image and this helps to re-rank the text based search results. Keywords are expanded based on the selected query image visual content that helps to capture user intention. Based on the expanded keywords image pool get expanded that contain more relevant images with the query image. Expanded keywords are also used to expand the query image that lead to multiple positive similar images and the similarity metrics are learned for page re-ranking. Re-ranking of similarity images to the query image based on photo quality assessment to provide better search results.

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5

Wu, Xian, Rui-Long Li, Fang-Lue Zhang, Jian-Cheng Liu, Jue Wang, Ariel Shamir, and Shi-Min Hu. "Deep Portrait Image Completion and Extrapolation." IEEE Transactions on Image Processing 29 (2020): 2344–55. http://dx.doi.org/10.1109/tip.2019.2945866.

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6

Esmersoy, Cengiz, and Michael Oristaglio. "Reverse‐time wave‐field extrapolation, imaging, and inversion." GEOPHYSICS 53, no. 7 (July 1988): 920–31. http://dx.doi.org/10.1190/1.1442529.

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The scattered wave field propagated backward in time into an arbitrary background medium is related via a volume integral to perturbations in velocity about the background, which are expressed as a scattering potential. In general, there is no closed‐form expression for the kernel of this integral representation, although it can be expressed asymptotically as a superposition of plane waves backpropagated from the receiver array. When the receiver array completely surrounds the scatterer, the kernel reduces to the imaginary part of the Green’s function for the background medium. This integral representation is used to relate the images obtained by imaging algorithms to the actual scattering potential. Two such relations are given: (1) for the migrated image, obtained by deconvolving the extrapolated field with the incident field; and (2) for the reconstructed image, obtained by applying a one‐way wave operator to the extrapolated field and then deconvolving by the incident field. The migrated image highlights rapid changes in the scattering potential (interfaces), whereas the reconstructed image can, under ideal conditions, be a perfect reconstruction of the scattering potential. “Ideal” conditions correspond to (1) weak scattering about a smoothly varying background medium, (2) a receiver array with full angular aperture, and (3) data of infinite bandwidth. Images obtained from a multioffset vertical seismic profile (VSP) illustrate some of the practical differences between the two imaging algorithms. The reconstructed image shows a much clearer picture of the target (a reef structure), in part because the one‐way imaging operator eliminates artifacts caused by the limited aperture of the receiver array.

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7

Seiler, J., and A. Kaup. "Complex-Valued Frequency Selective Extrapolation for Fast Image and Video Signal Extrapolation." IEEE Signal Processing Letters 17, no. 11 (November 2010): 949–52. http://dx.doi.org/10.1109/lsp.2010.2078504.

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8

Zhang, Xiaofeng, Feng Chen, Cailing Wang, Ming Tao, and Guo-Ping Jiang. "SiENet: Siamese Expansion Network for Image Extrapolation." IEEE Signal Processing Letters 27 (2020): 1590–94. http://dx.doi.org/10.1109/lsp.2020.3019705.

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9

Bobman, S. A., S. J. Riederer, J. N. Lee, T. Tasciyan, F. Farzaneh, and H. Z. Wang. "Pulse sequence extrapolation with MR image synthesis." Radiology 159, no. 1 (April 1986): 253–58. http://dx.doi.org/10.1148/radiology.159.1.3952314.

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10

Chang, Wen‐Fong, and George A. McMechan. "Reverse‐time migration of offset vertical seismic profiling data using the excitation‐time imaging condition." GEOPHYSICS 51, no. 1 (January 1986): 67–84. http://dx.doi.org/10.1190/1.1442041.

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Анотація:

To apply reverse‐time migration to prestack, finite‐offset data from variable‐velocity media, the standard (time zero) imaging condition must be generalized because each point in the image space has a different image time (or times). This generalization is the excitation‐time imaging condition, in which each point is imaged at the one‐way traveltime from the source to that point. Reverse‐time migration with the excitation‐time imaging condition consists of three elements: (1) computation of the imaging condition; (2) extrapolation of the recorder wave field; and (3) application of the imaging condition. Computation of the imaging condition for each point in the image is done by ray tracing from the source point; this is equivalent to extrapolation of the source wave field through the medium. Extrapolation of the recorded wave field is done by an acoustic finite‐difference algorithm. Imaging is performed at each step of the finite‐difference extrapolation by extracting, from the propagating wave field, the amplitude at each mesh point that is imaged at that time and adding these into the image space at the same spatial locations. The locus of all points imaged at one time step is a wavefront [a constant time (or phase) trajectory]. This prestack migration algorithm is very general. The excitation‐time imaging condition is applicable to all source‐receiver geometries and variable‐velocity media and reduces exactly to the usual time‐zero imaging condition when used with zero‐offset surface data. The algorithm is illustrated by application to both synthetic and real VSP data. The most interesting and potentially useful result in the processing of the synthetic data is imaging of the horizontal fluid interfaces within a reservoir even when the surrounding reservoir boundaries are not well imaged.

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11

Zhe, Jingping, and Stewart A. Greenhalgh. "Prestack multicomponent migration." GEOPHYSICS 62, no. 2 (March 1997): 598–613. http://dx.doi.org/10.1190/1.1444169.

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Prestack elastic migration by displacement potential extrapolation is a mixed, systematic, and function‐blocked vector wavefield migration algorithm. A new wavefield extrapolation method for inhomogeneous media is introduced here according to the following sequence: displacements ← potentials ← extrapolation of the potentials ← displacements, which is relatively accurate and not computer‐time intensive. Traveltimes of both direct downgoing P‐ and S‐waves, which are necessary in elastic migration, are calculated with a modified convolutional acoustic forward modeling program applicable to complex structures. A new image condition based on the time consistent principle is developed. It involves first obtaining an image condition section. Then two images (P P and S S) are obtained from the product of the extrapolated and decomposed P P‐ and S S‐wave displacement amplitudes and the image condition section. All P P‐, P S‐, S P‐ and S S‐waves are considered when the image condition section is calculated. The image condition section minimizes cross‐talk between modes. Compared to previous treatments, the newly developed image condition formula is superior since it allows migration of multicomponent seismic data produced using a combined P and S source. Numerical test results are very encouraging and clearly demonstrate the robustness of the technique. Further work is continuing so as to overcome ray angle and polarity problems in the image condition.

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12

Mousa, Wail A. "Prestack imaging of seismic data using Lp iterative reweighted least-squares wavefield extrapolation filters in the frequency-space domain." GEOPHYSICS 83, no. 4 (July 1, 2018): V243—V252. http://dx.doi.org/10.1190/geo2016-0498.1.

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A stable explicit depth wavefield extrapolation is obtained using [Formula: see text] iterative reweighted least-squares (IRLS) frequency-space ([Formula: see text]-[Formula: see text]) finite-impulse response digital filters. The problem of designing such filters to obtain stable images of challenging seismic data is formulated as an [Formula: see text] IRLS minimization. Prestack depth imaging of the challenging Marmousi model data set was then performed using the explicit depth wavefield extrapolation with the proposed [Formula: see text] IRLS-based algorithm. Considering the extrapolation filter design accuracy, the [Formula: see text] IRLS minimization method resulted in an image with higher quality when compared with the weighted least-squares method. The method can, therefore, be used to design high-accuracy extrapolation filters.

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13

Kim, Jin-Ju, and Si-Woong Lee. "Effective Exemplar-Based Image Inpainting Using Patch Extrapolation." Journal of the Korea Contents Association 14, no. 2 (February 28, 2014): 1–9. http://dx.doi.org/10.5392/jkca.2014.14.02.001.

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14

Rosman, Guy, Lorina Dascal, Avram Sidi, and Ron Kimmel. "Efficient Beltrami Image Filtering via Vector Extrapolation Methods." SIAM Journal on Imaging Sciences 2, no. 3 (January 2009): 858–78. http://dx.doi.org/10.1137/080728391.

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15

Kang, Yicheng. "Consistent Blind Image Deblurring Using Jump-Preserving Extrapolation." Journal of Computational and Graphical Statistics 29, no. 2 (October 16, 2019): 372–82. http://dx.doi.org/10.1080/10618600.2019.1665536.

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16

Greenspan, H., C. H. Anderson, and S. Akber. "Image enhancement by nonlinear extrapolation in frequency space." IEEE Transactions on Image Processing 9, no. 6 (June 2000): 1035–48. http://dx.doi.org/10.1109/83.846246.

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17

Westin, T. "PASS PROCESSING AND EXTRAPOLATION OF SPOT IMAGE GEOMETRY." Photogrammetric Record 13, no. 78 (August 26, 2006): 923–29. http://dx.doi.org/10.1111/j.1477-9730.1991.tb00760.x.

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18

NAGASAKA, Yosuke, Takuma FUNAHASHI, and Hiroyasu KOSHIMIZU. "Dynamic Range Compression for Extrapolation of Image Morphing." Journal of the Japan Society for Precision Engineering 80, no. 12 (2014): 1206–12. http://dx.doi.org/10.2493/jjspe.80.1206.

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19

Dapogny, Arnaud, Matthieu Cord, and Patrick Perez. "The Missing Data Encoder: Cross-Channel Image Completion with Hide-and-Seek Adversarial Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10688–95. http://dx.doi.org/10.1609/aaai.v34i07.6696.

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Image completion is the problem of generating whole images from fragments only. It encompasses inpainting (generating a patch given its surrounding), reverse inpainting/extrapolation (generating the periphery given the central patch) as well as colorization (generating one or several channels given other ones). In this paper, we employ a deep network to perform image completion, with adversarial training as well as perceptual and completion losses, and call it the “missing data encoder” (MDE). We consider several configurations based on how the seed fragments are chosen. We show that training MDE for “random extrapolation and colorization” (MDE-REC), i.e. using random channel-independent fragments, allows a better capture of the image semantics and geometry. MDE training makes use of a novel “hide-and-seek” adversarial loss, where the discriminator seeks the original non-masked regions, while the generator tries to hide them. We validate our models qualitatively and quantitatively on several datasets, showing their interest for image completion, representation learning as well as face occlusion handling.

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20

Gashnikov, M. V. "A differential image compression method using adaptive parameterized extrapolation." Optical Memory and Neural Networks 26, no. 2 (April 2017): 137–44. http://dx.doi.org/10.3103/s1060992x17020023.

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21

Aso, Takashi, Noriaki Suetake, and Takeshi Yamakawa. "A Weighted Linear Extrapolation-Based Simple Image Enlargement Algorithm." Intelligent Automation & Soft Computing 12, no. 3 (January 2006): 345–53. http://dx.doi.org/10.1080/10798587.2006.10642937.

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22

SHIMIZU, M., M. NAKASHIZUKA, and Y. IIGUNI. "Image Enlargement by Nonlinear Frequency Extrapolation with Morphological Operators." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 3 (March 1, 2008): 859–67. http://dx.doi.org/10.1093/ietfec/e91-a.3.859.

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23

Peng, Xiang, Pengdong Gao, Zeyi Liu, and Hanben Niu. "Data reduction and extrapolation for 3-D digital image." Optik 114, no. 6 (2003): 266–70. http://dx.doi.org/10.1078/0030-4026-00259.

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24

Suhyeon, Jeong, and Lee Seungkyu. "Class specific biased extrapolation of images in latent space for imbalanced image classification." Electronic Imaging 34, no. 10 (January 16, 2022): 191–1. http://dx.doi.org/10.2352/ei.2022.34.10.ipas-191.

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25

Yu, Pengfei, Jianhua Geng, Xiaobo Li, and Chenlong Wang. "Acoustic-elastic coupled equation for ocean bottom seismic data elastic reverse time migration." GEOPHYSICS 81, no. 5 (September 2016): S333—S345. http://dx.doi.org/10.1190/geo2015-0535.1.

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Анотація:

Conventionally, multicomponent geophones used to record the elastic wavefields in the solid seabed are necessary for ocean bottom seismic (OBS) data elastic reverse time migration (RTM). Particle velocity components are usually injected directly as boundary conditions in the elastic-wave equation in the receiver-side wavefield extrapolation step, which causes artifacts in the resulting elastic images. We have deduced a first-order acoustic-elastic coupled equation (AECE) by substituting pressure fields into the elastic velocity-stress equation (EVSE). AECE has three advantages for OBS data over EVSE when performing elastic RTM. First, the new equation unifies wave propagation in acoustic and elastic media. Second, the new equation separates P-waves directly during wavefield propagation. Third, three approaches are identified when using the receiver-side multicomponent particle velocity records and pressure records in elastic RTM processing: (1) particle velocity components are set as boundary conditions in receiver-side vectorial extrapolation with the AECE, which is equal to the elastic RTM using the conventional EVSE; (2) the pressure component may also be used for receiver-side scalar extrapolation with the AECE, and with which we can accomplish PP and PS images using only the pressure records and suppress most of the artifacts in the PP image with vectorial extrapolation; and (3) ocean-bottom 4C data can be simultaneously used for elastic images with receiver-side tensorial extrapolation using the AECE. Thus, the AECE may be used for conventional elastic RTM, but it also offers the flexibility to obtain PP and PS images using only pressure records.

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26

Sava, Paul, and Sergey Fomel. "Time-shift imaging condition in seismic migration." GEOPHYSICS 71, no. 6 (November 2006): S209—S217. http://dx.doi.org/10.1190/1.2338824.

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Seismic imaging based on single-scattering approximation is in the analysis of the match between the source and receiver wavefields at every image location. Wavefields at depth are functions of space and time and are reconstructed from surface data either by integral methods (Kirchhoff migration) or by differential methods (reverse-time or wavefield extrapolation migration). Different methods can be used to analyze wavefield matching, of which crosscorrelation is a popular option. Implementation of a simple imaging condition requires time crosscorrelation of source and receiver wavefields, followed by extraction of the zero time lag. A generalized imaging condition operates by crosscorrelation in both space and time, followed by image extraction at zero time lag. Images at different spatial crosscorrelation lags are indicators of imaging accuracy and are also used for image-angle decomposition. In this paper, we introduce an alternative prestack imaging condition in which we preserve multiple lags of the time crosscorrelation. Prestack images are described as functions of time shifts as opposed to space shifts between source and receiver wavefields. This imaging condition is applicable to migration by Kirchhoff, wavefield extrapolation, or reverse-time techniques. The transformation allows construction of common-image gathers presented as functions of either time shift or reflection angle at every location in space. Inaccurate migration velocity is revealed by angle-domain common-image gathers with nonflat events. Computational experiments using a synthetic data set from a complex salt model demonstrate the main features of the method.

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27

Naghadeh, Diako Hariri, and Mohamad Ali Riahi. "One-way wave-equation migration in log-polar coordinates." GEOPHYSICS 78, no. 2 (March 1, 2013): S59—S67. http://dx.doi.org/10.1190/geo2012-0229.1.

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We obtained acoustic wave and wavefield extrapolation equations in log-polar coordinates (LPCs) and tried to enhance the imaging. To achieve this goal, it was necessary to decrease the angle between the wavefield extrapolation axis and wave propagation direction in the one-way wave-equation migration (WEM). If we were unable to carry it out, more reflection wave energy would be lost in the migration process. It was concluded that the wavefield extrapolation operator in LPCs at low frequencies has a large wavelike region, and at high frequencies, it can mute the evanescent energy. In these coordinate systems, an extrapolation operator can readily lend itself to high-order finite-difference schemes; therefore, even with the use of inexpensive operators, WEM in LPCs can clearly image varied (horizontal and vertical) events in complex geologic structures using wide-angle and turning waves. In these coordinates, we did not encounter any problems with reflections from opposing dips. Dispersion played important roles not only as a filter operator but also as a gain function. Prestack and poststack migration results were obtained with extrapolation methods in different coordinate systems, and it was concluded that migration in LPCs can image steeply dipping events in a much better way when compared with other methods.

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28

Poulton, Mary M., Ben K. Sternberg, and Charles E. Glass. "Location of subsurface targets in geophysical data using neural networks." GEOPHYSICS 57, no. 12 (December 1992): 1534–44. http://dx.doi.org/10.1190/1.1443221.

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Neural networks were used to estimate the offset, depth, and conductivity‐area product of a conductive target given an electromagnetic ellipticity image of the target. Five different neural network paradigms and five different representations of the ellipticity image were compared. The networks were trained with synthetic images of the target and tested on field data and more synthetic data. The extrapolation capabilities of the networks were also tested with synthetic data lying outside the spatial limits of the training set. The data representations consisted of the whole image, the subsampled image, the peak and adjacent troughs, the peak, and components from a two‐dimensional (2-D) fast Fourier transform. The paradigms tested were standard back propagation, directed random search, functional link, extended delta bar delta, and the hybrid combination of self‐organizing map and back propagation. For input patterns with less than 100 elements, the directed random search and functional link networks gave the best results. For patterns with more than 100 elements, self‐organizing map to back propagation was most accurate. Using the whole ellipticity image gave the most accurate results for all the network paradigms. The fast Fourier transform data representation also yielded good results with a much faster computation time. Average accuracies of offset, depth, and conductivity‐area product as high as 97 percent could be achieved for test and field data and 88 percent for extrapolation data.

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29

Ponti, Moacir, Elias S. Helou, Paulo Jorge S. G. Ferreira, and Nelson D. A. Mascarenhas. "Image Restoration Using Gradient Iteration and Constraints for Band Extrapolation." IEEE Journal of Selected Topics in Signal Processing 10, no. 1 (February 2016): 71–80. http://dx.doi.org/10.1109/jstsp.2015.2493978.

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30

Ponti-Jr, Moacir P., Nelson D. A. Mascarenhas, Paulo J. S. G. Ferreira, and Claudio A. T. Suazo. "Three-dimensional noisy image restoration using filtered extrapolation and deconvolution." Signal, Image and Video Processing 7, no. 1 (February 22, 2011): 1–10. http://dx.doi.org/10.1007/s11760-011-0216-x.

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31

Zhang, Lele, Evert Slob, Joost van der Neut, and Kees Wapenaar. "Artifact-free reverse time migration." GEOPHYSICS 83, no. 5 (September 1, 2018): A65—A68. http://dx.doi.org/10.1190/geo2017-0795.1.

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Анотація:

We have derived an improved reverse time migration (RTM) scheme to image the medium without artifacts arising from internal multiple reflections. This is based on a revised implementation of Marchenko redatuming using a new time-truncation operator. Because of the new truncation operator, we can use the time-reversed version of the standard wavefield-extrapolation operator as initial estimate for retrieving the upgoing focusing function. Then, the retrieved upgoing focusing function can be used to directly image the medium by correlating it with the standard wavefield-extrapolation operator. This imaging scheme can be seen as an artifact-free RTM scheme with two terms. The first term gives the conventional RTM image with the wrong amplitude and artifacts due to internal multiple reflections. The second term gives a correction image that can be used to correct the amplitude and remove artifacts in the image generated by the first term. We evaluated the success of the method with a 2D numerical example.

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32

Guerra, Claudio, and Biondo Biondi. "Fast 3D migration-velocity analysis by wavefield extrapolation using the prestack exploding-reflector model." GEOPHYSICS 76, no. 5 (September 2011): WB151—WB167. http://dx.doi.org/10.1190/geo2010-0384.1.

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In areas of complex geology, migration-velocity estimation should use methods that describe the complexity of wavefield propagation, such as focusing and defocusing, multipathing, and frequency-dependent velocity sensitivity. Migration-velocity analysis by wavefield extrapolation has the ability to address these issues because, in contrast to ray-based methods, it uses wavefields as carriers of information. However, its high cost and lack of flexibility with respect to model parametrization and to target-oriented analysis have prevented its routine industrial use. We overcome those limitations by using new types of wavefields as carriers of information: the image-space generalized wavefields. These wavefields are synthesized from a prestack image computed with wavefield-extrapolation methods, using the prestack exploding-reflector model. Cost of migration-velocity analysis (MVA) by wavefield extrapolation is decreased because only a small number of image-space generalized wavefields are necessary to accurately describe the kinematics of velocity errors and because these wavefields can be easily used in a target-oriented way. Flexibility is naturally incorporated because modeling these wavefields has as the initial conditions selected reflectors, which allow use of a horizon-based parametrization of the model space. In a 3D example of the North Sea, we show that using wavefields synthesized by the prestack exploding-reflector model greatly improves efficiency of MVA by wavefield extrapolation, while yielding a final migration-velocity model that is accurate as evidenced by well focused and structurally reasonable reflectors.

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33

Klaser, Kerstin, Pedro Borges, Richard Shaw, Marta Ranzini, Marc Modat, David Atkinson, Kris Thielemans, et al. "A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis." Applied Sciences 11, no. 4 (February 12, 2021): 1667. http://dx.doi.org/10.3390/app11041667.

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Synthesising computed tomography (CT) images from magnetic resonance images (MRI) plays an important role in the field of medical image analysis, both for quantification and diagnostic purposes. Convolutional neural networks (CNNs) have achieved state-of-the-art results in image-to-image translation for brain applications. However, synthesising whole-body images remains largely uncharted territory, involving many challenges, including large image size and limited field of view, complex spatial context, and anatomical differences between images acquired at different times. We propose the use of an uncertainty-aware multi-channel multi-resolution 3D cascade network specifically aiming for whole-body MR to CT synthesis. The Mean Absolute Error on the synthetic CT generated with the MultiResunc network (73.90 HU) is compared to multiple baseline CNNs like 3D U-Net (92.89 HU), HighRes3DNet (89.05 HU) and deep boosted regression (77.58 HU) and shows superior synthesis performance. We ultimately exploit the extrapolation properties of the MultiRes networks on sub-regions of the body.

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34

Ferguson, Robert J., and Gary F. Margrave. "Prestack depth migration by symmetric nonstationary phase shift." GEOPHYSICS 67, no. 2 (March 2002): 594–603. http://dx.doi.org/10.1190/1.1468620.

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A new depth migration method suitable for heterogeneous media is presented. The well‐known phase shift plus interpolation (PSPI) method and the recently introduced nonstationary phase‐shift (NSPS) method are combined into a single symmetric operator with improved accuracy and stability and with similar computational effort. For prestack depth migration, the symmetric operator is used in a recursive wavefield extrapolation to compute incident and reflected wavefields at any desired depth, and the ratio of the incident and reflected wavefields at a particular depth is used to estimate seismic reflectivity. When the velocity model is made piecewise constant laterally, the symmetric extrapolation operator can be computed efficiently using ordinary phase‐shift extrapolation for a series of reference velocities and appropriate spatial windowing. Migration of the Marmousi synthetic data set by symmetric nonstationary phase shift (SNPS) provides an image that compares favorably with an image of the zero‐offset reflectivity derived from the Marmousi velocity model.

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35

Umbanhowar, Charles E., and James A. Umbanhowar. "Improving the efficiency of sediment charcoal image analysis." Holocene 31, no. 7 (March 24, 2021): 1229–33. http://dx.doi.org/10.1177/09596836211003226.

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Imaging of charcoal particles extracted from lake sediments provides an important way to understand past fire regimes. Imaging of large numbers of particles can be time consuming. In this note we explore the effects of subsampling and extrapolation of area on estimates of sum charcoal area, using resampling of real and simulated data sets and propose a protocol in which all particles are counted with only the first 100 encountered being imaged. Extrapolated estimates of sum total area of charcoal for 40 real samples were nearly identical to actual values, and error introduced due to subsampling was low (Coefficient of variation <0.2) for all but samples originally containing fewer than 50 particles. Similarly, error was low for simulated data (CV <0.02). Extrapolation provided better estimates of charcoal area than did a regression-based approach. Our results suggest that imaging a fixed number of pieces of charcoal ( n = 100) and counting any additional pieces represents a time efficient way to estimate charcoal area while at the same time retaining useful information on particle size and shape

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36

Chen, Xiumei, Zaitian Ma, Guohua Nie, and Huazhong Wang. "Target-oriented curved-wave prestack depth migration by controlled illumination." GEOPHYSICS 74, no. 4 (July 2009): S95—S104. http://dx.doi.org/10.1190/1.3137018.

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Conventional prestack depth migration (PSDM) based on full prestack data involves many computations for wavefield extrapolation. Areal shot-record technology offers an attractive alternative for efficient PSDM because its synthesis process greatly decreases the amount of prestack data required for migration. Constructing its synthesis operator is key to the image quality of the migrated areal shot record. Curved-wave PSDM technology expands areal shot-record migration. It constructs synthesis operators by defining a complex function as a base kernel of curved wavefields multiplied by a factor of its illumination perturbation related to ray parameter and then synthesizes curved wavefields for migrations. Based on curved-wave-migration theory, we propose an efficient and accurate curved-wave, controlled-illumination method to migrate in a target-oriented way. We construct tar-get-oriented synthesis operators by wave theory, combined with rotation or perturbation of the illuminating direction of a base-kernel synthesis operator. The base-kernel operator is obtained by inverse extrapolation of a predefined source wavefield at the given target level. Different model-constrained synthesis operators are constructed by rotating or perturbing illumination of the kernel operator. Then they are applied to shot records to synthesize curved-wave records. The resulting curved wavefields are entered for depth extrapolation and imaging, combining image results from different curved wavefields to produce the total image. The method controls the illumination of synthesized source wavefields in a target-oriented way directly at the surface and can achieve high-quality images of the target zone with great efficiency. Numerical demonstrations on the standard Marmousi model provide good imaging results of the complicated structure.

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37

Ang, Andersen Man Shun, and Nicolas Gillis. "Accelerating Nonnegative Matrix Factorization Algorithms Using Extrapolation." Neural Computation 31, no. 2 (February 2019): 417–39. http://dx.doi.org/10.1162/neco_a_01157.

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We propose a general framework to accelerate significantly the algorithms for nonnegative matrix factorization (NMF). This framework is inspired from the extrapolation scheme used to accelerate gradient methods in convex optimization and from the method of parallel tangents. However, the use of extrapolation in the context of the exact coordinate descent algorithms tackling the nonconvex NMF problems is novel. We illustrate the performance of this approach on two state-of-the-art NMF algorithms: accelerated hierarchical alternating least squares and alternating nonnegative least squares, using synthetic, image, and document data sets.

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38

Zhu, Xinfa, and George A. McMechan. "Stretch-free migration imaging condition." GEOPHYSICS 78, no. 4 (July 1, 2013): S203—S210. http://dx.doi.org/10.1190/geo2012-0519.1.

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Анотація:

Prestack migration has angle-dependent wavelet stretch effects, which lowers the image resolution at large reflection angles. Most current stretch correction methods operate on the migrated images. We develop a new stretch-free imaging condition, which does a shrink-and-shift operation on the extracted propagation wavelet after extrapolation, but before the imaging condition is applied. The algorithm is illustrated with the excitation amplitude imaging condition; the new images show successful stretch corrections over wide angle apertures, and preserve amplitude and phase.

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39

Wang, Yanghua. "Inverse- Q filtered migration." GEOPHYSICS 73, no. 1 (January 2008): S1—S6. http://dx.doi.org/10.1190/1.2806924.

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Анотація:

An inverse-[Formula: see text] filtered migration algorithm performs seismic migration and inverse-[Formula: see text] filtering simultaneously, in which the latter compensates for the amplitudes and corrects the phase distortions resulting from the earth attenuation effect. However, the amplitudes of high-frequency components grow rapidly in the extrapolation procedure, so numerical instability is a concern when including the inverse-[Formula: see text] filter in the migration. The instability for each frequency component is independent of data and is affected only by migration models. The stabilization problem may be treated separately from the wavefield-extrapolation scheme. The proposed strategy is to construct supersedent of attenuation coefficients, based on given velocity and [Formula: see text] models, before performing wavefield extrapolation in the space-frequency domain. This stabilized algorithm for inverse-[Formula: see text] filtered migration is applicable to subsurface media with vertical and lateral variations in velocity and [Formula: see text] functions. It produces a seismic image with enhanced resolution and corrected timing, comparable to an ideal image without the earth attenuation effect.

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40

Sun, Junzhe, Sergey Fomel, and Lexing Ying. "Low-rank one-step wave extrapolation for reverse time migration." GEOPHYSICS 81, no. 1 (January 1, 2016): S39—S54. http://dx.doi.org/10.1190/geo2015-0183.1.

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Reverse time migration (RTM) relies on accurate wave extrapolation engines to image complex subsurface structures. To construct such operators with high efficiency and numerical stability, we have developed a one-step wave extrapolation approach using complex-valued low-rank decomposition to approximate the mixed-domain space-wavenumber wave extrapolation symbol. The low-rank one-step method involves a complex-valued phase function, which is more flexible than a real-valued phase function of two-step schemes, and thus it is capable of modeling a wider variety of dispersion relations. Two novel designs of the phase function leads to the desired properties in wave extrapolation. First, for wave propagation in inhomogeneous media, including a velocity gradient term assures a more accurate phase behavior, particularly when the velocity variations are large. Second, an absorbing boundary condition, which is propagation-direction-dependent, can be incorporated into the phase function as an anisotropic attenuation term. This term allows waves to travel parallel to the boundary without absorption, thus reducing artificial reflections at wide incident angles. Using numerical experiments, we revealed the stability improvement of a one-step scheme in comparison with two-step schemes. We observed the low-rank one-step operator to be remarkably stable and capable of propagating waves using large time step sizes, even beyond the Nyquist limit. The stability property can help to minimize the computational cost of seismic modeling or RTM. The low-rank one-step wave extrapolation also handles anisotropic wave propagation accurately and efficiently. When applied to RTM in anisotropic media, the proposed method generated high-quality images.

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41

Visalli, Roberto, Gaetano Ortolano, Gaston Godard, and Rosolino Cirrincione. "Micro-Fabric Analyzer (MFA): A New Semiautomated ArcGIS-Based Edge Detector for Quantitative Microstructural Analysis of Rock Thin-Sections." ISPRS International Journal of Geo-Information 10, no. 2 (January 27, 2021): 51. http://dx.doi.org/10.3390/ijgi10020051.

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Micro-Fabric Analyzer (MFA) is a new GIS-based tool for the quantitative extrapolation of rock microstructural features that takes advantage both of the characteristics of the X-ray images and the optical image features. Most of the previously developed edge mineral grain detectors are uniquely based on the physical properties of the X-ray-, electron-, or optical-derived images; not permitting the exploitation of the specific physical properties of each image type at the same time. More advanced techniques, such as 3D microtomography, permit the reconstruction of tridimensional models of mineral fabric arrays, even though adjacent mineral grain boundaries with the same atomic density are often not detectable. Only electron backscatter diffraction (EBSD) allows providing high-performing grain boundary detection that is crystallographically differentiated per mineral phase, even though it is relatively expensive and can be executed only in duly equipped microanalytical laboratories by suitably trained users. Instead, the MFA toolbox allows quantifying fabric parameters subdivided per mineral type starting from a crossed-polarizers high-resolution RGB image, which is useful for identifying the edges of the individual grains characterizing rock fabrics. Then, this image is integrated with a set of micro-X-ray maps, which are useful for the quantitative extrapolation of elemental distribution maps. In addition, all this is achieved by means of low-cost and easy-to-use equipment. We applied the tool on amphibolite, mylonitic-paragneiss, and -tonalite samples to extrapolate the particle fabric on different metamorphic rock types, as well as on the same sandstone sample used for another edge detector, which is useful for comparing the obtained results.

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42

Vasconcelos, Ivan, Matteo Ravasi, and Joost van der Neut. "Subsurface-domain, interferometric objective functions for target-oriented waveform inversion." GEOPHYSICS 82, no. 4 (July 1, 2017): A37—A41. http://dx.doi.org/10.1190/geo2016-0608.1.

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Resolving details in subsurface reservoir parameters from surface waveform data is a challenging problem, particularly when a reservoir is beneath a complex overburden whose properties are also poorly known. We have developed new metrics for detecting and quantifying errors in subsurface models that are well-suited for target-oriented inversion. Our metric, when combined with state-of-the art redatuming, aims at enabling waveform inversion for target volume parameters with no need to resolve model features elsewhere (e.g., in the overburden). We refer to these metrics as “interferometric objective functions” because they rely on extrapolation from reciprocity integrals commonly used in seismic interferometry. As in seismic interferometry, wavefield extrapolation retrieves the wave response between two points by combining observed data with an extrapolator that describes the response between the subsurface and the data boundary. When the source point is outside a target volume, either forward time or reverse time extrapolation produces the same field. However, because they physically rely on different components from the boundary data, the forward time and reverse time extrapolated fields are only equal when the model used is consistent with the real subsurface within the target volume. As such, we use the difference between the forward time and reverse time extrapolated fields to define subsurface-domain metrics that quantify model errors. Our approach thus provides a new metric for target-oriented nonlinear inversion in the subsurface domain, one that fundamentally differs from other subsurface domain metrics based on, e.g., focusing or image extensions.

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43

Rosner, Hans-Joachim. "Spatial extrapolation of near-ground air temperatures using digital image processing." Meteorologische Zeitschrift 3, no. 3 (July 11, 1994): 155–62. http://dx.doi.org/10.1127/metz/3/1994/155.

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44

Cheng, Hu, and Feng Huang. "Magnetic resonance imaging image intensity correction with extrapolation and adaptive smoothing." Magnetic Resonance in Medicine 55, no. 4 (2006): 959–66. http://dx.doi.org/10.1002/mrm.20841.

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45

Lee, James N., Stephen J. Riederer, Stuart A. Bobman, Jeffrey P. Johnson, and Farhad Farzaneh. "The precision of T R extrapolation in magnetic resonance image synthesis." Medical Physics 13, no. 2 (March 1986): 170–76. http://dx.doi.org/10.1118/1.595954.

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46

Effland, Alexander, Martin Rumpf, and Florian Schäfer. "Image Extrapolation for the Time Discrete Metamorphosis Model: Existence and Applications." SIAM Journal on Imaging Sciences 11, no. 1 (January 2018): 834–62. http://dx.doi.org/10.1137/17m1129544.

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47

REED, A. H., R. B. PANDEY, and D. L. LAVOIE. "FRACTAL DIMENSIONALITY OF PORE AND GRAIN VOLUME OF A SILICICLASTIC MARINE SAND." International Journal of Modern Physics C 11, no. 08 (December 2000): 1555–59. http://dx.doi.org/10.1142/s0129183100001358.

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Three-dimensional (3D) spatial distributions of pore and grain volumes were determined from high-resolution computer tomography (CT) images of resin-impregnated marine sands. Using a linear gradient extrapolation method, cubic three-dimensional samples were constructed from two-dimensional CT images. Image porosity (0.37) was found to be consistent with the estimate of porosity by water weight loss technique (0.36). Scaling of the pore volume (Vp) with the linear size (L), V ~ LD provides the fractal dimensionalities of the pore volume (D = 2.74 ± 0.02) and grain volume (D = 2.90 ± 0.02) typical for sedimentary materials.

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48

Hu, Yuan, Lei Chen, Zhibin Wang, Xiang Pan, and Hao Li. "Towards a More Realistic and Detailed Deep-Learning-Based Radar Echo Extrapolation Method." Remote Sensing 14, no. 1 (December 22, 2021): 24. http://dx.doi.org/10.3390/rs14010024.

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Deep-learning-based radar echo extrapolation methods have achieved remarkable progress in the precipitation nowcasting field. However, they suffer from a common notorious problem—they tend to produce blurry predictions. Although some efforts have been made in recent years, the blurring problem is still under-addressed. In this work, we propose three effective strategies to assist deep-learning-based radar echo extrapolation methods to achieve more realistic and detailed prediction. Specifically, we propose a spatial generative adversarial network (GAN) and a spectrum GAN to improve image fidelity. The spatial and spectrum GANs aim at penalizing the distribution discrepancy between generated and real images from the spatial domain and spectral domain, respectively. In addition, a masked style loss is devised to further enhance the details by transferring the detailed texture of ground truth radar sequences to extrapolated ones. We apply a foreground mask to prevent the background noise from transferring to the outputs. Moreover, we also design a new metric termed the power spectral density score (PSDS) to quantify the perceptual quality from a frequency perspective. The PSDS metric can be applied as a complement to other visual evaluation metrics (e.g., LPIPS) to achieve a comprehensive measurement of image sharpness. We test our approaches with both ConvLSTM baseline and U-Net baseline, and comprehensive ablation experiments on the SEVIR dataset show that the proposed approaches are able to produce much more realistic radar images than baselines. Most notably, our methods can be readily applied to any deep-learning-based spatiotemporal forecasting models to acquire more detailed results.

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49

KUKLINSKI, WALTER S. "UTILIZATION OF FRACTAL IMAGE MODELS IN MEDICAL IMAGE PROCESSING." Fractals 02, no. 03 (September 1994): 363–69. http://dx.doi.org/10.1142/s0218348x94000454.

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One of the more successful engineering applications of fractal geometry has been the utilization of fractal image models in medical image processing. These applications have included tissue characterization studies, textural image segmentation, and image restoration using fractal constraints. The class of fractal models used in medical image processing and the techniques used to estimate the fractal dimension associated with these models will be reviewed. An image segmentation algorithm that utilized a fractal textural feature and formulated the segmentation process as a configurational optimization problem is presented. The configurational optimization method allows information about both, the degree of correspondence between a candidate segment and an assumed textural model, and morphological information about the candidate segment to be used in the segmentation process. To apply this configurational optimization technique with a fractal textural model however, requires the estimation of the fractal dimension of an irregularly shaped candidate segment. The potential utility of a discrete Gerchberg-Papoulis bandlimited extrapolation algorithm to the estimation of the fractal dimension of an irregularly shaped candidate segment is also discussed.

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50

Decker, Luke, Dmitrii Merzlikin, and Sergey Fomel. "Diffraction imaging and time-migration velocity analysis using oriented velocity continuation." GEOPHYSICS 82, no. 2 (March 1, 2017): U25—U35. http://dx.doi.org/10.1190/geo2016-0141.1.

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We perform seismic diffraction imaging and time-migration velocity analysis by separating diffractions from specular reflections and decomposing them into slope components. We image the slope components using migration velocity extrapolation in time-space-slope coordinates. The extrapolation is described by a convection-type partial differential equation and implemented in a highly parallel manner in the Fourier domain. Synthetic and field data experiments show that the proposed algorithms are able to detect accurate time-migration velocities by measuring the flatness of diffraction events in slope gathers for single- and multiple-offset data.

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