Impainting with a Nonlocal Means Filter
The need to fill areas of an image distorted by artifacts with texture from undistorted areas is called impainting. Impainting is used both to improve the visual perception of an image and in classical recognition and robotics problems in order to remove irrelevant information from an image. Modern...
Saved in:
Published in | Journal of communications technology & electronics Vol. 67; no. 6; pp. 722 - 727 |
---|---|
Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Moscow
Pleiades Publishing
01.06.2022
Springer Springer Nature B.V |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | The need to fill areas of an image distorted by artifacts with texture from undistorted areas is called impainting. Impainting is used both to improve the visual perception of an image and in classical recognition and robotics problems in order to remove irrelevant information from an image. Modern methods of impainting use neural networks. However, these approaches have drawbacks that do not allow the use of these algorithms in the practice of computer vision. In this article, we propose to use a nonlocal means (NLM) filter, which has proven itself to be excellent in image noise reduction tasks. The basis of our motivation is the fact that the goal of the NLM, like any method for recovering images distorted by noise, is to minimize the distance (or error by some criterion) between the original image and the reconstructed one. The result of computer experiments showed that the proposed method of impainting is superior to other methods according to peak signal-to-noise ratio (PSNR) criterion. The effectiveness of the proposed filter is also shown with the help of illustrations to the article, so that the reader can compare the quality of different processing options visually. |
---|---|
AbstractList | The need to fill areas of an image distorted by artifacts with texture from undistorted areas is called impainting. Impainting is used both to improve the visual perception of an image and in classical recognition and robotics problems in order to remove irrelevant information from an image. Modern methods of impainting use neural networks. However, these approaches have drawbacks that do not allow the use of these algorithms in the practice of computer vision. In this article, we propose to use a nonlocal means (NLM) filter, which has proven itself to be excellent in image noise reduction tasks. The basis of our motivation is the fact that the goal of the NLM, like any method for recovering images distorted by noise, is to minimize the distance (or error by some criterion) between the original image and the reconstructed one. The result of computer experiments showed that the proposed method of impainting is superior to other methods according to peak signal-to-noise ratio (PSNR) criterion. The effectiveness of the proposed filter is also shown with the help of illustrations to the article, so that the reader can compare the quality of different processing options visually. The need to fill areas of an image distorted by artifacts with texture from undistorted areas is called impainting. Impainting is used both to improve the visual perception of an image and in classical recognition and robotics problems in order to remove irrelevant information from an image. Modern methods of impainting use neural networks. However, these approaches have drawbacks that do not allow the use of these algorithms in the practice of computer vision. In this article, we propose to use a nonlocal means (NLM) filter, which has proven itself to be excellent in image noise reduction tasks. The basis of our motivation is the fact that the goal of the NLM, like any method for recovering images distorted by noise, is to minimize the distance (or error by some criterion) between the original image and the reconstructed one. The result of computer experiments showed that the proposed method of impainting is superior to other methods according to peak signal-to-noise ratio (PSNR) criterion. The effectiveness of the proposed filter is also shown with the help of illustrations to the article, so that the reader can compare the quality of different processing options visually. |
Audience | Academic |
Author | Kober, V. I. Karnaukhov, V. N. Zimina, L. V. Mozerov, M. G. |
Author_xml | – sequence: 1 givenname: V. N. surname: Karnaukhov fullname: Karnaukhov, V. N. email: vnk@iitp.ru organization: Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences – sequence: 2 givenname: V. I. surname: Kober fullname: Kober, V. I. email: vitaly@iitp.ru organization: Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences – sequence: 3 givenname: M. G. surname: Mozerov fullname: Mozerov, M. G. email: mozer@iitp.ru organization: Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences – sequence: 4 givenname: L. V. surname: Zimina fullname: Zimina, L. V. email: ziminalv@gmail.com organization: Moscow Polytechnic University |
BookMark | eNp1kV1LwzAUQINMcJv-AN8KPohgZ5Lmo30cw-lgKjh9LmmbdBldOpMU9d-bUUGHSCAJuefk3uSOwMC0RgJwjuAEoYTcrBBkBGOWYQwZRDA7AkNEKY0ZpXwQ9iEc7-MnYOTcBsIkYzAZgsvFdie08drU0bv260hEj61p2lI00YMUxkVz3XhpT8GxEo2TZ9_rGLzOb19m9_Hy6W4xmy7jMqHMx2kqi0pyrqCiVPFKsqTKMo4UUQilNEmEEDwtCKZCEowKykkopCw44iURpErG4KK_d2fbt046n2_azpqQMscshQwRCHmgJj1Vi0bm2qjWW1GGUcmtLsPPKB3OpxymmOA0o0G4OhAC4-WHr0XnXL5YPR-y17_YonPaSBcmp-u1d71ygKMeL23rnJUq31m9FfYzRzDf9yb_05vg4N5xgTW1tD-v_F_6Ahtfjgk |
CitedBy_id | crossref_primary_10_1016_j_geoen_2024_212744 crossref_primary_10_1134_S1064226922120063 |
Cites_doi | 10.1109/ICCV.1999.790383 10.1109/TIP.2015.2492822 10.1134/S1064226918120070 10.1007/11744085_44 10.1109/TIP.2007.901238 10.1145/1360612.1360666 10.1134/S106422691612010X 10.1145/218380.218446 10.1117/1.OE.55.2.023101 10.1109/TIP.2019.2892668 10.1145/2010324.1964964 10.1145/1141911.1142018 10.1109/TIP.2015.2395820 10.1145/1276377.1276441 10.1111/j.1467-8659.2009.01645.x 10.1109/CVPR.2005.38 10.1109/CVPR.2013.159 10.1109/TIP.2013.2244221 10.1109/CVPR.2017.728 10.1145/237170.237264 10.1117/1.1606459 10.1109/TIP.2014.2387390 10.1109/TIP.2017.2705427 |
ContentType | Journal Article |
Copyright | Pleiades Publishing, Inc. 2022. ISSN 1064-2269, Journal of Communications Technology and Electronics, 2022, Vol. 67, No. 6, pp. 722–727. © Pleiades Publishing, Inc., 2022. Russian Text © The Author(s), 2021, published in Informatsionnye Protsessy, 2021, Vol. 21, No. 4, pp. 244–252. COPYRIGHT 2022 Springer |
Copyright_xml | – notice: Pleiades Publishing, Inc. 2022. ISSN 1064-2269, Journal of Communications Technology and Electronics, 2022, Vol. 67, No. 6, pp. 722–727. © Pleiades Publishing, Inc., 2022. Russian Text © The Author(s), 2021, published in Informatsionnye Protsessy, 2021, Vol. 21, No. 4, pp. 244–252. – notice: COPYRIGHT 2022 Springer |
DBID | AAYXX CITATION N95 XI7 ISR 7SP 8FD L7M |
DOI | 10.1134/S1064226922060109 |
DatabaseName | CrossRef Gale_Business Insights: Global Business Insights: Essentials Gale In Context: Science Electronics & Communications Abstracts Technology Research Database Advanced Technologies Database with Aerospace |
DatabaseTitle | CrossRef Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts |
DatabaseTitleList | Technology Research Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering |
EISSN | 1555-6557 |
EndPage | 727 |
ExternalDocumentID | A708242895 10_1134_S1064226922060109 |
GroupedDBID | -5B -5G -BR -EM -Y2 -~C .4S .DC .VR 06D 0R~ 0VY 1N0 29K 29~ 2J2 2JN 2JY 2KG 2KM 2LR 2VQ 2~H 30V 3V. 4.4 408 40D 40E 5GY 5VS 6NX 7WY 88I 8AO 8FE 8FG 8FL 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AAFGU AAHNG AAIAL AAJKR AANZL AARHV AARTL AATNV AATVU AAUYE AAWCG AAYFA AAYIU AAYQN AAYTO ABBBX ABDZT ABECU ABFGW ABFTV ABHLI ABHQN ABJNI ABJOX ABKAS ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACBEA ACBMV ACBRV ACBXY ACBYP ACGFO ACGFS ACGOD ACHSB ACHXU ACIGE ACIPQ ACKNC ACMDZ ACMLO ACOKC ACOMO ACREN ACSNA ACTTH ACVWB ACWMK ADHHG ADHIR ADINQ ADKNI ADKPE ADMDM ADOXG ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEBTG AEFTE AEGAL AEGNC AEJHL AEJRE AEKMD AENEX AEOHA AEPYU AESTI AETLH AEVLU AEVTX AEXYK AFFNX AFGCZ AFKRA AFLOW AFNRJ AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGGBP AGJBK AGMZJ AGQMX AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIIXL AILAN AIMYW AITGF AJBLW AJDOV AJRNO AKQUC ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AZFZN AZQEC B-. BA0 BAAKF BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ CAG CCPQU COF CS3 CSCUP D-I DDRTE DNIVK DPUIP DU5 DWQXO EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC G8K GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GROUPED_ABI_INFORM_COMPLETE HCIFZ HF~ HG6 HLICF HMJXF HRMNR HVGLF HZ~ IAO IJ- IKXTQ ISR IWAJR IXD I~X I~Z J-C JBSCW JZLTJ K60 K6~ KOV LLZTM M0C M2P M4Y MA- MK~ ML~ N2Q N95 NB0 NPVJJ NQJWS NU0 O9- O93 O9J P62 P9P PF0 PQBIZ PQQKQ PROAC PT4 Q2X QOS R89 R9I RNS ROL RSV S16 S1Z S27 S3B SAP SDH SEG SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TSG TUC TUS UG4 UNUBA UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 XI7 XU3 YLTOR Z7R Z7X Z83 Z88 ZMTXR ~A9 AACDK AAJBT AASML AAYXX ABAKF ACAOD ACDTI ACZOJ AEFQL AEMSY AFBBN AGRTI AIGIU CITATION H13 PQBZA 7SP 8FD L7M |
ID | FETCH-LOGICAL-c356t-88ebde77f0f55f7de63d9971f4f118533aaa78b425ae421b574396cb717c4a4d3 |
IEDL.DBID | AGYKE |
ISSN | 1064-2269 |
IngestDate | Thu Oct 10 16:05:35 EDT 2024 Tue May 28 06:10:37 EDT 2024 Sat Sep 28 20:54:15 EDT 2024 Tue Oct 08 15:54:25 EDT 2024 Wed Oct 02 14:36:10 EDT 2024 Sat Dec 16 12:07:46 EST 2023 |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 6 |
Keywords | nonlocal means filter image restoration enhancement and contrasting of image details impainting |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c356t-88ebde77f0f55f7de63d9971f4f118533aaa78b425ae421b574396cb717c4a4d3 |
PQID | 2680614007 |
PQPubID | 54180 |
PageCount | 6 |
ParticipantIDs | proquest_journals_2680614007 gale_infotracacademiconefile_A708242895 gale_incontextgauss_ISR_A708242895 gale_businessinsightsgauss_A708242895 crossref_primary_10_1134_S1064226922060109 springer_journals_10_1134_S1064226922060109 |
PublicationCentury | 2000 |
PublicationDate | 2022-06-01 |
PublicationDateYYYYMMDD | 2022-06-01 |
PublicationDate_xml | – month: 06 year: 2022 text: 2022-06-01 day: 01 |
PublicationDecade | 2020 |
PublicationPlace | Moscow |
PublicationPlace_xml | – name: Moscow – name: Silver Spring |
PublicationTitle | Journal of communications technology & electronics |
PublicationTitleAbbrev | J. Commun. Technol. Electron |
PublicationYear | 2022 |
Publisher | Pleiades Publishing Springer Springer Nature B.V |
Publisher_xml | – name: Pleiades Publishing – name: Springer – name: Springer Nature B.V |
References | D. Karras and G. Mertzios, “Discretization schemes and numerical approximations of pde impainting models and a comparative evaluation on novel real world mri reconstruction applications,” in 2004 IEEE Int. Workshop on Imaging Systems and Techniques (IST) (IEEE Cat. No. 04EX896), Stresa, Italy, May 14,2004 (IEEE, New York, 2004), pp. 153–158. U. Demir and G. Unal, “Patch-based image inpainting with generative adversarial networks,” arXiv Preprint arXiv:1803.07422. 2018. R. A. Yeh, C. Chen, Lim T. Yian, et al., “Semantic image inpainting with deep generative models,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii,2017 (IEEE, New York, 2017), pp. 5485–5493. MozerovM.Constrained optical flow estimation as a matching problemIEEE Trans. Image Process.20132220442055306164110.1109/TIP.2013.2244221 LiuG.ZhongH.JiaoL.Comparing noisy patches for image denoising: A double noise similarity modelIEEE Trans. Image Process.201524862872330584710.1109/TIP.2014.2387390 KarnaukhovV.MozerovM.Fast non-local mean filter algorithm based on recursive calculation of similarity weightsJ. Commun. Technol. Electron.2018631475147710.1134/S1064226918120070 KarnaukhovV.MozerovM.Restoration of multispectral images by the gradient reconstruction method and estimation of the blur parameters on the basis of the multipurpose matching modelJ. Commun. Technol. Electron.2016611426143110.1134/S106422691612010X ErshovE.KarnaukhovV.MozerovM.Probabilistic choice between symmetric disparities in motion stereo matching for a lateral navigation systemOpt. Eng.20165502310102310110.1117/1.OE.55.2.023101 S. Paris and F. Durand, “A fast approximation of the bilateral filter using a signal processing approach,” in Proc. Eur. Conf. on Computer Vision (ECCV), Graz, Austria, May 7–13,2006 (ECCV, 2006), pp. 568–580. R. Fattal, M. Agrawala, and S. Rusinkiewicz, “Multiscale shape and detail enhancement from multi-light image collections,” ACM Trans. Graph. (TOG) / ACM 26, 51 (2007). GastalE.OliveiraM.Domain transform for edge-aware image and video processingACM Trans. Graph.2011306910.1145/2010324.1964964 A. Adams, J. Baek, and M. Davis, “Fast high-dimensional filtering using the permutohedral lattice,” Comp. Graph. Forum 29, 753–762 (2010). MozerovM. G.van de WeijerJ.Global color sparseness and a local statistics prior for fast bilateral filteringIEEE Trans. Image Process.20152458425853342381210.1109/TIP.2015.2492822 MozerovM.van de WeijerJ.Improved recursive geodesic distance computation for edge preserving filterIEEE Trans. Image Process.20172636963706366274410.1109/TIP.2017.2705427 MozerovM. G.van de WeijerJ.One-view occlusion detection for stereo matching with a fully connected crf modelIEEE Trans. Image Process.20192829362947394075110.1109/TIP.2019.2892668 D. J. Heeger and J. R. Bergen, in Pyramid-Sased Texture Analysis/Synthesis (Proc. 22nd Ann. Conf. on Computer Graphics and Interactive Techniques,1995), pp. 229–238. A. N. Hirani and T. Totsuka, “Combining frequency and spatial domain information for fast interactive image noise removal,” in Proc. 23rd Ann. Conf. Computer Graphics and Interactive Techniques, New Orleans, Louisiana, Aug. 4–9,1996 (Ass. Comp. Mach., New York, 1996), pp. 269–276. W. Zuo, L. Zhang, C. Song, and D. Zhang, “Texture enhanced image denoising via gradient histogram preservation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Washington, June 23–28,2013 (IEEE, New York, 2013), pp. 1203–1210. A. A. Efros and T. K. Leung, in Texture Synthesis by Non-Parametric Sampling (Proc. 7th IEEE Int. Conf. on Computer Vision, Fort Collins, Colorado,1999) (IEEE, New York, 1999), Vol. 2, pp. 1033–1038. A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” in Proc. IEEE Comp. Soc. Conf. Computer Vision and Pattern Recognition (CVPR), San Diego, June 20–26,2005 (IEEE, New York, 2005), Vol. 2, pp. 60–65. Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski, “Edge-preserving decompositions for multi-scale tone and detail manipulation,” ACM Trans. Graph. (TOG) / ACM 27, 67 (2008). MozerovM.van de WeijerJ.Accurate stereo matching by two-step energy minimizationIEEE Trans. Image Process.20152411531163331161510.1109/TIP.2015.2395820 RamanathR.SnyderW. E.Adaptive demosaickingJ. Electron. Imag.20031263364210.1117/1.1606459 H. Winnemöller, S. Olsen, and B. Gooch, “Real-time video abstraction,” ACM Trans. Graph. 25, 1221–1226 (2006). C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in CVPR (1998), pp. 839–846. RužićT.PižuricaA.Context-aware patch-based image inpainting using markov random field modelingIEEE Trans. Image Process.20142344445633006801408.94565 DabovK.FoiA.KatkovnikV.EgiazarianK.Image denoising by sparse 3-d transform-domain collaborative filteringIEEE Trans. Image Process.20071620802095246062610.1109/TIP.2007.901238 1745_CR23 M. Mozerov (1745_CR13) 2013; 22 1745_CR25 V. Karnaukhov (1745_CR27) 2018; 63 V. Karnaukhov (1745_CR16) 2016; 61 cr-split#-1745_CR4.1 1745_CR20 cr-split#-1745_CR4.2 R. Ramanath (1745_CR17) 2003; 12 G. Liu (1745_CR26) 2015; 24 M. Mozerov (1745_CR9) 2015; 24 1745_CR15 1745_CR14 1745_CR19 1745_CR18 1745_CR2 1745_CR1 E. Gastal (1745_CR21) 2011; 30 M. G. Mozerov (1745_CR10) 2019; 28 1745_CR6 M. G. Mozerov (1745_CR22) 2015; 24 1745_CR3 E. Ershov (1745_CR11) 2016; 55 M. Mozerov (1745_CR12) 2017; 26 K. Dabov (1745_CR24) 2007; 16 1745_CR8 1745_CR7 T. Ružić (1745_CR5) 2014; 23 |
References_xml | – ident: 1745_CR6 – ident: 1745_CR8 – ident: 1745_CR2 doi: 10.1109/ICCV.1999.790383 – ident: #cr-split#-1745_CR4.1 – volume: 24 start-page: 5842 year: 2015 ident: 1745_CR22 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2015.2492822 contributor: fullname: M. G. Mozerov – volume: 23 start-page: 444 year: 2014 ident: 1745_CR5 publication-title: IEEE Trans. Image Process. contributor: fullname: T. Ružić – volume: 63 start-page: 1475 year: 2018 ident: 1745_CR27 publication-title: J. Commun. Technol. Electron. doi: 10.1134/S1064226918120070 contributor: fullname: V. Karnaukhov – ident: 1745_CR19 doi: 10.1007/11744085_44 – volume: 16 start-page: 2080 year: 2007 ident: 1745_CR24 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2007.901238 contributor: fullname: K. Dabov – ident: 1745_CR15 doi: 10.1145/1360612.1360666 – volume: 61 start-page: 1426 year: 2016 ident: 1745_CR16 publication-title: J. Commun. Technol. Electron. doi: 10.1134/S106422691612010X contributor: fullname: V. Karnaukhov – ident: 1745_CR3 doi: 10.1145/218380.218446 – volume: 55 start-page: 023101 year: 2016 ident: 1745_CR11 publication-title: Opt. Eng. doi: 10.1117/1.OE.55.2.023101 contributor: fullname: E. Ershov – volume: 28 start-page: 2936 year: 2019 ident: 1745_CR10 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2019.2892668 contributor: fullname: M. G. Mozerov – volume: 30 start-page: 69 year: 2011 ident: 1745_CR21 publication-title: ACM Trans. Graph. doi: 10.1145/2010324.1964964 contributor: fullname: E. Gastal – ident: #cr-split#-1745_CR4.2 – ident: 1745_CR18 doi: 10.1145/1141911.1142018 – volume: 24 start-page: 1153 year: 2015 ident: 1745_CR9 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2015.2395820 contributor: fullname: M. Mozerov – ident: 1745_CR14 doi: 10.1145/1276377.1276441 – ident: 1745_CR20 doi: 10.1111/j.1467-8659.2009.01645.x – ident: 1745_CR23 doi: 10.1109/CVPR.2005.38 – ident: 1745_CR25 doi: 10.1109/CVPR.2013.159 – volume: 22 start-page: 2044 year: 2013 ident: 1745_CR13 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2013.2244221 contributor: fullname: M. Mozerov – ident: 1745_CR7 doi: 10.1109/CVPR.2017.728 – ident: 1745_CR1 doi: 10.1145/237170.237264 – volume: 12 start-page: 633 year: 2003 ident: 1745_CR17 publication-title: J. Electron. Imag. doi: 10.1117/1.1606459 contributor: fullname: R. Ramanath – volume: 24 start-page: 862 year: 2015 ident: 1745_CR26 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2014.2387390 contributor: fullname: G. Liu – volume: 26 start-page: 3696 year: 2017 ident: 1745_CR12 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2705427 contributor: fullname: M. Mozerov |
SSID | ssj0039603 |
Score | 2.2966902 |
Snippet | The need to fill areas of an image distorted by artifacts with texture from undistorted areas is called impainting. Impainting is used both to improve the... |
SourceID | proquest gale crossref springer |
SourceType | Aggregation Database Publisher |
StartPage | 722 |
SubjectTerms | Algorithms Buildings Communications Engineering Computer vision Criteria Engineering Image reconstruction Machine vision Mathematical Models and Computational Methods Networks Neural networks Noise control Noise reduction Object recognition Remodeling, restoration, etc Robotics Signal to noise ratio Visual perception |
Title | Impainting with a Nonlocal Means Filter |
URI | https://link.springer.com/article/10.1134/S1064226922060109 https://www.proquest.com/docview/2680614007 |
Volume | 67 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JS8NAFH5IvejBXawbQRRBSW1mJpPkWMW6YQ8uoKcwW0SEVEx68dc7b5KotXrwkkvehMz29vc9gF3GqDJBkPg6UrHPlEx8K8cDXweSKM10VziH2_WAn9-zy4fwYQrIp-sif-k0EUnHqKu2I-zoNkBVmfCEEIQQwZq96RDxvlow3Tt7vDpt-C-1OnmVVs-ZjwPqWOavHxmTRj958kRw1Mmc_nxVB1g4qEJMNXnpjErZUe-TQI7_mM4CzNUqqNerzswiTJl8CWa_ARMuw_6F5RLPromEh55aT3iDYe7knndtrHTz-s8YZl-B-_7p3cm5X7dU8BUNeenHsZHaRFHWzcIwi7ThVCdJFGQsC1ByUyFEFEt7kYVhJJAh2itcSWv0KSaYpqvQyoe5WQMv1iZhOiN2EGXW6JMRF11KDQKqUaF5Gw6apU1fK-SM1FkclKUTs2_DHi5-WnfetI8CfRPFkxgVRdqLrKJi7aQkbMOOo0PkihxTYyqCi9ubMaL9migblm9CibrSwP43gl2NUW42m53Wd7dICY_RTLbKUxsOm837ev3nHNb_Rb0BMwQrKZxDZxNa5dvIbFn9ppTb9kD3j48H2_XB_gDO6uxR |
link.rule.ids | 315,786,790,27955,27956,41114,42183,52144 |
linkProvider | Springer Nature |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JS8NAFH5IPagHd7GuQRRBSW0yk0lyLGJt1fagFfQUZouIkIpJL_565yUTtC4HL7nkTchs73v7AziklEjtebGrQhm5VIrYNTjuucoTvlRUtXlpcBsMWe-eXj0EDzaPO6-j3WuXZMmpq74j9OzOQ1nZZ7HvYw0RTNqbpQjwDZjtXD5eX9QMmBihvIqrZ9TFAdaZ-etHpuDoO1P-4R0tQae7BKP6d6tYk5fWpBAt-f6tkuM_57MMi1YIdTrVqVmBGZ2twsKX0oRrcNw3fOK5bCPhoK3W4c5wnJXI5wy0wTen-4yO9nW4716MznuubargShKwwo0iLZQOw7SdBkEaKs2IiuPQS2nqIXYTznkYCXOVuTbLKwLUWJgURu2TlFNFNqCRjTO9CU6kdExV6ptBhBq1T4SMtwnRWFKNcMWacFKvbfJa1c5ISp2D0OTH7JtwhKuf2N6b5pGjdSJ_4pM8TzqhEVWMphQHTTgo6bB2RYbBMRVB_-52iujYEqXj4o1LbnMNzH9juaspyp16txN7e_PEZxEqykZ8asJpvXmfr_-cw9a_qPdhrjca3CQ3_eH1Nsz7mFdRmnd2oFG8TfSukXYKsWdP9wdhIu6p |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JS8QwFH7ICKIHd3FciyiCUp02adoeB3V0XAZxAT3VbBUROmI7F3-9eW2KjstBvPTSl5L0JXn79wA2KSVSe17sqlBGLpUido0c91zlCV8qqlq8dLhd9NjJLT29C-5sn9O8znavQ5JVTQOiNGXF_otKbQ8Sun_tod7ss9j3EU8EC_hGqTm1tAGj7eP7s6P6MiZGQa9y7Bl1cYANbP74kSHR9PWC_hYpLQVQZwoe6qlXeSfPe4NC7Mm3L6iO_1jbNExa5dRpV7tpBkZ0NgsTnyAL52C7a-6Pp7K9hIM-XIc7vX5WSkTnQhu553SeMAA_D7edo5uDE9c2W3AlCVjhRpEWSodh2kqDIA2VZkTFceilNPVQphPOeRgJc8S5pr4nArRkmBTGHJSUU0UWoJH1M70ITqR0TFXqm0GEGnNQhIy3CNEItUa4Yk3Yqf9z8lJhaiSlLUJo8m31TdhCTiS2J6d55Oi1yB_5IM-TdmhUGGNBxUETNko6xLTIMGmmIuheXw0RbVuitF-8csltDYKZN8JgDVGu1JxP7KnOE59FaEAbtaoJuzUjP17_uoalP1Gvw9jlYSc57_bOlmHcx3KL0uuzAo3idaBXjRJUiDW70d8Blej3hA |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Impainting+with+a+Nonlocal+Means+Filter&rft.jtitle=Journal+of+communications+technology+%26+electronics&rft.au=Karnaukhov%2C+V.+N&rft.au=Kober%2C+V.+I&rft.au=Mozerov%2C+M.+G&rft.au=Zimina%2C+L.+V&rft.date=2022-06-01&rft.pub=Springer&rft.issn=1064-2269&rft.volume=67&rft.issue=6&rft.spage=722&rft_id=info:doi/10.1134%2FS1064226922060109&rft.externalDBID=ISR&rft.externalDocID=A708242895 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1064-2269&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1064-2269&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1064-2269&client=summon |