LEADER 01323nam 2200373 a 450 001 9910700330403321 005 20191003123907.0 035 $a(CKB)5470000002409685 035 $a(OCoLC)726861904 035 $a(EXLCZ)995470000002409685 100 $a20110524d2010 ua 0 101 0 $aeng 135 $aurcn||||a|||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 04$aThe docs$b[electronic resource] $ea graphic novel /$fproduced by the Naval Health Research Center ; prepared by RTI International ; authors, Heidi Kraft ... [and others] 210 1$a[San Diego, Calif.] :$c[Naval Health Research Center],$d[2010] 215 $a1 online resource (196 pages, 4 unnumbered pages) $ccolor illustrations 300 $a"First printing April 2010." 320 $aIncludes bibliographical references (page [199]). 517 $aDocs 606 $aIraq War, 2003-2011$xMedical care$vComic books, strips, etc 608 $aGraphic novels.$2lcgft 615 0$aIraq War, 2003-2011$xMedical care 701 $aKraft$b Heidi Squier$01394245 712 02$aNaval Health Research Center (U.S.) 712 02$aRTI International. 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910700330403321 996 $aThe docs$93451346 997 $aUNINA LEADER 00939nam0 22002531i 450 001 UON00196698 005 20231205103236.864 100 $a20030730d1957 |0itac50 ba 101 $arus 102 $aSU 105 $a|||| ||||| 200 1 $aIl'ja Efimovi? Repin$f[sost. al'boma i avtor vstup. stat'ti N. G. Ma?kovcev] 210 $aMoskva$cIZOGIZ$d1957 215 $a103 p.$ctav.$d34 cm. 620 $aRU$dMoskva$3UONL003152 676 $a759.7$cPittura russa. 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Emre Celebi 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (392 pages) 225 1 $aUnsupervised and Semi-Supervised Learning,$x2522-8498 311 08$a9783031681059 311 08$a3031681053 327 $aChapter 1 Introduction to Super-Resolution for Remotely Sensed Hyperspectral Images -- Chapter 2 Real-World Unsupervised Remote Sensing Image Super-Resolution: Addressing Challenges, Solution and Future Prospects -- Chapter 3 Advancements in Deep Learning-Based Super-Resolution for Remote Sensing: A Comprehensive Review and Future Directions -- Chapter 4 Multi-Image Super-Resolution Using Graph Neural Networks -- Chapter 5 Effectiveness Analysis of Example-Based Machine Learning and Deep Learning Methods for Super-Resolution Hyperspectral Images -- Chapter 6 Synergy of Images: Multi-Image Fusion Empowering Super-Resolution in Remote Sensing -- Chapter 7 Unsupervised Pansharpening using ConvNets -- Chapter 8 A comprehensive overview of satellite image fusion: From classical model-based to cutting-edge deep learning approaches -- Chapter 9 Super-Resolution for Spectral Image. 330 $aThis book provides a comprehensive perspective over the landscape of super-resolution techniques developed for and applied to remotely-sensed images. The chapters tackle the most important problems that professionals face when dealing with super-resolution in the context of remote sensing. These are: evaluation procedures to assess the super-resolution quality; benchmark datasets (simulated and real-life); super-resolution for specific data modalities (e.g., panchromatic, multispectral, and hyperspectral images); single-image super-resolution, including generative adversarial networks; multi-image fusion (temporal and/or spectral); real-world super-resolution; and task-driven super-resolution. The book presents the results of several recent surveys on super-resolution specifically for the remote sensing community. Focuses on reconstruction accuracy compared with ground truth rather than on generating a visually-attractive outcome; Explains how to apply super-resolution to a variety of image modalities inherent to remote sensing; Gathers the description of training datasets and benchmarks that are based on remotely-sensed images. 410 0$aUnsupervised and Semi-Supervised Learning,$x2522-8498 606 $aComputational intelligence 606 $aTelecommunication 606 $aComputer vision 606 $aComputational Intelligence 606 $aCommunications Engineering, Networks 606 $aComputer Vision 615 0$aComputational intelligence. 615 0$aTelecommunication. 615 0$aComputer vision. 615 14$aComputational Intelligence. 615 24$aCommunications Engineering, Networks. 615 24$aComputer Vision. 676 $a006.3 700 $aKawulok$b Michal$01766939 701 $aKawulok$b Jolanta$01766940 701 $aSmolka$b Bogdan$01766941 701 $aCelebi$b M. 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