Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-04-20

AUTHORS

Yifan Song, David Nakath, Mengkun She, Kevin Köser

ABSTRACT

Visual systems are receiving increasing attention in underwater applications. While the photogrammetric and computer vision literature so far has largely targeted shallow water applications, recently also deep sea mapping research has come into focus. The majority of the seafloor, and of Earth’s surface, is located in the deep ocean below 200 m depth, and is still largely uncharted. Here, on top of general image quality degradation caused by water absorption and scattering, additional artificial illumination of the survey areas is mandatory that otherwise reside in permanent darkness as no sunlight reaches so deep. This creates unintended non-uniform lighting patterns in the images and non-isotropic scattering effects close to the camera. If not compensated properly, such effects dominate seafloor mosaics and can obscure the actual seafloor structures. Moreover, cameras must be protected from the high water pressure, e.g. by housings with thick glass ports, which can lead to refractive distortions in images. Additionally, no satellite navigation is available to support localization. All these issues render deep sea visual mapping a challenging task and most of the developed methods and strategies cannot be directly transferred to the seafloor in several kilometers depth. In this survey we provide a state of the art review of deep ocean mapping, starting from existing systems and challenges, discussing shallow and deep water models and corresponding solutions. Finally, we identify open issues for future lines of research. More... »

PAGES

243-267

References to SciGraph publications

  • 2006-08-23. Development and application of a video-mosaic survey technology to document the status of coral reef communities in ENVIRONMENTAL MONITORING AND ASSESSMENT
  • 2005. Color Correction of Underwater Images for Aquatic Robot Inspection in ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION
  • 2016. Autonomous Underwater Vehicle Navigation in SPRINGER HANDBOOK OF OCEAN ENGINEERING
  • 2016-03-16. Return to Antikythera: Multi-session SLAM Based AUV Mapping of a First Century B.C. Wreck Site in FIELD AND SERVICE ROBOTICS
  • 2000. Underwater Camera Calibration in COMPUTER VISION — ECCV 2000
  • 2004-01. Imaging Coral I: Imaging Coral Habitats with the SeaBED AUV in SENSING AND IMAGING
  • 2021-02-23. Optimization of Multi-LED Setups for Underwater Robotic Vision Systems in PATTERN RECOGNITION. ICPR INTERNATIONAL WORKSHOPS AND CHALLENGES
  • 2011. A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image in COMPUTER VISION – ACCV 2010
  • 2005. Lens Model Selection for Visual Tracking in PATTERN RECOGNITION
  • 2006-12-19. Automatic Panoramic Image Stitching using Invariant Features in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2019-10-24. Challenges in Underwater Visual Navigation and SLAM in AI TECHNOLOGY FOR UNDERWATER ROBOTS
  • 2011. Efficient Large-Scale Stereo Matching in COMPUTER VISION – ACCV 2010
  • 1992-08. A theory of self-calibration of a moving camera in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2020-10-30. Underwater image restoration based on secondary guided transmission map in MULTIMEDIA TOOLS AND APPLICATIONS
  • 2017-08-21. New approaches to high-resolution mapping of marine vertical structures in SCIENTIFIC REPORTS
  • 2021-02-23. Deep Sea Robotic Imaging Simulator in PATTERN RECOGNITION. ICPR INTERNATIONAL WORKSHOPS AND CHALLENGES
  • 2002-07. Vision and the Atmosphere in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2019-05-29. Biological effects 26 years after simulated deep-sea mining in SCIENTIFIC REPORTS
  • 2018-12-19. Underwater-GAN: Underwater Image Restoration via Conditional Generative Adversarial Network in PATTERN RECOGNITION AND INFORMATION FORENSICS
  • 1977-12. The retinex theory of color vision. in SCIENTIFIC AMERICAN
  • 2019-10-25. Adjustment and Calibration of Dome Port Camera Systems for Underwater Vision in PATTERN RECOGNITION
  • 2012-04-25. Fast image dehazing using guided joint bilateral filter in THE VISUAL COMPUTER
  • 2003-04. A Variational Framework for Retinex in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2012. Refractive Calibration of Underwater Cameras in COMPUTER VISION – ECCV 2012
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s41064-022-00206-y

    DOI

    http://dx.doi.org/10.1007/s41064-022-00206-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1147256097


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Oceanic Machine Vision, GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148, Kiel, Germany", 
              "id": "http://www.grid.ac/institutes/grid.15649.3f", 
              "name": [
                "Oceanic Machine Vision, GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148, Kiel, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Song", 
            "givenName": "Yifan", 
            "id": "sg:person.010155657025.69", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010155657025.69"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Oceanic Machine Vision, GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148, Kiel, Germany", 
              "id": "http://www.grid.ac/institutes/grid.15649.3f", 
              "name": [
                "Oceanic Machine Vision, GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148, Kiel, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Nakath", 
            "givenName": "David", 
            "id": "sg:person.012633261257.13", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012633261257.13"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Oceanic Machine Vision, GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148, Kiel, Germany", 
              "id": "http://www.grid.ac/institutes/grid.15649.3f", 
              "name": [
                "Oceanic Machine Vision, GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148, Kiel, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "She", 
            "givenName": "Mengkun", 
            "id": "sg:person.016152757367.11", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016152757367.11"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Oceanic Machine Vision, GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148, Kiel, Germany", 
              "id": "http://www.grid.ac/institutes/grid.15649.3f", 
              "name": [
                "Oceanic Machine Vision, GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148, Kiel, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "K\u00f6ser", 
            "givenName": "Kevin", 
            "id": "sg:person.012321336324.12", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012321336324.12"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-030-30683-0_11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1122015579", 
              "https://doi.org/10.1007/978-3-030-30683-0_11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/b:ssta.0000018445.25977.f3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019662077", 
              "https://doi.org/10.1023/b:ssta.0000018445.25977.f3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1022314423998", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005564593", 
              "https://doi.org/10.1023/a:1022314423998"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00371-012-0679-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051502328", 
              "https://doi.org/10.1007/s00371-012-0679-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-45053-x_42", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018932029", 
              "https://doi.org/10.1007/3-540-45053-x_42"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-019-44492-w", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1115994617", 
              "https://doi.org/10.1038/s41598-019-44492-w"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10661-006-9239-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016059929", 
              "https://doi.org/10.1007/s10661-006-9239-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-030-68790-8_29", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1135654073", 
              "https://doi.org/10.1007/978-3-030-68790-8_29"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-19315-6_3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009780475", 
              "https://doi.org/10.1007/978-3-642-19315-6_3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-030-68790-8_30", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1135653521", 
              "https://doi.org/10.1007/978-3-030-68790-8_30"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-030-05792-3_7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1110765748", 
              "https://doi.org/10.1007/978-3-030-05792-3_7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-33715-4_61", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014004696", 
              "https://doi.org/10.1007/978-3-642-33715-4_61"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11263-006-0002-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049205751", 
              "https://doi.org/10.1007/s11263-006-0002-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-16649-0_14", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008050171", 
              "https://doi.org/10.1007/978-3-319-16649-0_14"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-19309-5_39", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047331563", 
              "https://doi.org/10.1007/978-3-642-19309-5_39"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11585978_5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1046787860", 
              "https://doi.org/10.1007/11585978_5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11550518_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010438544", 
              "https://doi.org/10.1007/11550518_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-27702-8_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038546534", 
              "https://doi.org/10.1007/978-3-319-27702-8_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-030-33676-9_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1122090658", 
              "https://doi.org/10.1007/978-3-030-33676-9_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/s41598-017-09382-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091255960", 
              "https://doi.org/10.1038/s41598-017-09382-z"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1016328200723", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010390647", 
              "https://doi.org/10.1023/a:1016328200723"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/scientificamerican1277-108", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1056644898", 
              "https://doi.org/10.1038/scientificamerican1277-108"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00127171", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040466580", 
              "https://doi.org/10.1007/bf00127171"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11042-020-10049-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1132207931", 
              "https://doi.org/10.1007/s11042-020-10049-7"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2022-04-20", 
        "datePublishedReg": "2022-04-20", 
        "description": "Visual systems are receiving increasing attention in underwater applications. While the photogrammetric and computer vision literature so far has largely targeted shallow water applications, recently also deep sea mapping research has come into focus. The majority of the seafloor, and of Earth\u2019s surface, is located in the deep ocean below 200 m depth, and is still largely uncharted. Here, on top of general image quality degradation caused by water absorption and scattering, additional artificial illumination of the survey areas is mandatory that otherwise reside in permanent darkness as no sunlight reaches so deep. This creates unintended non-uniform lighting patterns in the images and non-isotropic scattering effects close to the camera. If not compensated properly, such effects dominate seafloor mosaics and can obscure the actual seafloor structures. Moreover, cameras must be protected from the high water pressure, e.g. by housings with thick glass ports, which can lead to refractive distortions in images. Additionally, no satellite navigation is available to support localization. All these issues render deep sea visual mapping a challenging task and most of the developed methods and strategies cannot be directly transferred to the seafloor in several kilometers depth. In this survey we provide a state of the art review of deep ocean mapping, starting from existing systems and challenges, discussing shallow and deep water models and corresponding solutions. Finally, we identify open issues for future lines of research.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s41064-022-00206-y", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.8101872", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1320463", 
            "issn": [
              "2512-2789", 
              "2512-2819"
            ], 
            "name": "PFG \u2013 Journal of Photogrammetry, Remote Sensing and Geoinformation Science", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "3", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "90"
          }
        ], 
        "keywords": [
          "computer vision literature", 
          "image restoration techniques", 
          "image quality degradation", 
          "vision literature", 
          "open issues", 
          "challenging task", 
          "visual mapping", 
          "refractive distortion", 
          "quality degradation", 
          "comprehensive survey", 
          "underwater applications", 
          "lighting patterns", 
          "camera", 
          "satellite navigation", 
          "ocean mapping", 
          "images", 
          "visual system", 
          "mapping research", 
          "corresponding solutions", 
          "navigation", 
          "restoration techniques", 
          "mapping", 
          "photogrammetric", 
          "applications", 
          "task", 
          "system", 
          "issues", 
          "future lines", 
          "shallow water applications", 
          "art review", 
          "artificial illumination", 
          "challenges", 
          "seafloor structures", 
          "research", 
          "technique", 
          "solution", 
          "top", 
          "Earth's surface", 
          "model", 
          "method", 
          "illumination", 
          "distortion", 
          "ports", 
          "localization", 
          "attention", 
          "strategies", 
          "focus", 
          "mosaic", 
          "state", 
          "survey", 
          "area", 
          "literature", 
          "patterns", 
          "structure", 
          "imaging", 
          "depth", 
          "survey area", 
          "lines", 
          "optical imaging", 
          "review", 
          "seafloor", 
          "degradation", 
          "majority", 
          "water model", 
          "surface", 
          "water application", 
          "high water pressure", 
          "deep ocean", 
          "kilometers depth", 
          "permanent darkness", 
          "scattering effect", 
          "effect", 
          "water pressure", 
          "housing", 
          "Ocean", 
          "sunlight", 
          "such effects", 
          "darkness", 
          "pressure", 
          "scattering", 
          "water absorption", 
          "absorption"
        ], 
        "name": "Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey", 
        "pagination": "243-267", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1147256097"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s41064-022-00206-y"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s41064-022-00206-y", 
          "https://app.dimensions.ai/details/publication/pub.1147256097"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:44", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_942.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s41064-022-00206-y"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s41064-022-00206-y'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s41064-022-00206-y'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s41064-022-00206-y'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s41064-022-00206-y'


     

    This table displays all metadata directly associated to this object as RDF triples.

    258 TRIPLES      21 PREDICATES      130 URIs      98 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s41064-022-00206-y schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Nd1312ed5044546839295992054e458e7
    4 schema:citation sg:pub.10.1007/11550518_6
    5 sg:pub.10.1007/11585978_5
    6 sg:pub.10.1007/3-540-45053-x_42
    7 sg:pub.10.1007/978-3-030-05792-3_7
    8 sg:pub.10.1007/978-3-030-30683-0_11
    9 sg:pub.10.1007/978-3-030-33676-9_6
    10 sg:pub.10.1007/978-3-030-68790-8_29
    11 sg:pub.10.1007/978-3-030-68790-8_30
    12 sg:pub.10.1007/978-3-319-16649-0_14
    13 sg:pub.10.1007/978-3-319-27702-8_4
    14 sg:pub.10.1007/978-3-642-19309-5_39
    15 sg:pub.10.1007/978-3-642-19315-6_3
    16 sg:pub.10.1007/978-3-642-33715-4_61
    17 sg:pub.10.1007/bf00127171
    18 sg:pub.10.1007/s00371-012-0679-y
    19 sg:pub.10.1007/s10661-006-9239-0
    20 sg:pub.10.1007/s11042-020-10049-7
    21 sg:pub.10.1007/s11263-006-0002-3
    22 sg:pub.10.1023/a:1016328200723
    23 sg:pub.10.1023/a:1022314423998
    24 sg:pub.10.1023/b:ssta.0000018445.25977.f3
    25 sg:pub.10.1038/s41598-017-09382-z
    26 sg:pub.10.1038/s41598-019-44492-w
    27 sg:pub.10.1038/scientificamerican1277-108
    28 schema:datePublished 2022-04-20
    29 schema:datePublishedReg 2022-04-20
    30 schema:description Visual systems are receiving increasing attention in underwater applications. While the photogrammetric and computer vision literature so far has largely targeted shallow water applications, recently also deep sea mapping research has come into focus. The majority of the seafloor, and of Earth’s surface, is located in the deep ocean below 200 m depth, and is still largely uncharted. Here, on top of general image quality degradation caused by water absorption and scattering, additional artificial illumination of the survey areas is mandatory that otherwise reside in permanent darkness as no sunlight reaches so deep. This creates unintended non-uniform lighting patterns in the images and non-isotropic scattering effects close to the camera. If not compensated properly, such effects dominate seafloor mosaics and can obscure the actual seafloor structures. Moreover, cameras must be protected from the high water pressure, e.g. by housings with thick glass ports, which can lead to refractive distortions in images. Additionally, no satellite navigation is available to support localization. All these issues render deep sea visual mapping a challenging task and most of the developed methods and strategies cannot be directly transferred to the seafloor in several kilometers depth. In this survey we provide a state of the art review of deep ocean mapping, starting from existing systems and challenges, discussing shallow and deep water models and corresponding solutions. Finally, we identify open issues for future lines of research.
    31 schema:genre article
    32 schema:isAccessibleForFree true
    33 schema:isPartOf N2592ad0ee29d412e9b52903bc04d7399
    34 Nddd8d7eecb5f41c195d227a5be89cccc
    35 sg:journal.1320463
    36 schema:keywords Earth's surface
    37 Ocean
    38 absorption
    39 applications
    40 area
    41 art review
    42 artificial illumination
    43 attention
    44 camera
    45 challenges
    46 challenging task
    47 comprehensive survey
    48 computer vision literature
    49 corresponding solutions
    50 darkness
    51 deep ocean
    52 degradation
    53 depth
    54 distortion
    55 effect
    56 focus
    57 future lines
    58 high water pressure
    59 housing
    60 illumination
    61 image quality degradation
    62 image restoration techniques
    63 images
    64 imaging
    65 issues
    66 kilometers depth
    67 lighting patterns
    68 lines
    69 literature
    70 localization
    71 majority
    72 mapping
    73 mapping research
    74 method
    75 model
    76 mosaic
    77 navigation
    78 ocean mapping
    79 open issues
    80 optical imaging
    81 patterns
    82 permanent darkness
    83 photogrammetric
    84 ports
    85 pressure
    86 quality degradation
    87 refractive distortion
    88 research
    89 restoration techniques
    90 review
    91 satellite navigation
    92 scattering
    93 scattering effect
    94 seafloor
    95 seafloor structures
    96 shallow water applications
    97 solution
    98 state
    99 strategies
    100 structure
    101 such effects
    102 sunlight
    103 surface
    104 survey
    105 survey area
    106 system
    107 task
    108 technique
    109 top
    110 underwater applications
    111 vision literature
    112 visual mapping
    113 visual system
    114 water absorption
    115 water application
    116 water model
    117 water pressure
    118 schema:name Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey
    119 schema:pagination 243-267
    120 schema:productId N44fbc1d48f014c5d9afcacd169db567a
    121 Nb1f77f21dbce4bb481f99fdafe8c4c40
    122 schema:sameAs https://app.dimensions.ai/details/publication/pub.1147256097
    123 https://doi.org/10.1007/s41064-022-00206-y
    124 schema:sdDatePublished 2022-12-01T06:44
    125 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    126 schema:sdPublisher Nd53edb8ef1684e7187c52ce3be5a3080
    127 schema:url https://doi.org/10.1007/s41064-022-00206-y
    128 sgo:license sg:explorer/license/
    129 sgo:sdDataset articles
    130 rdf:type schema:ScholarlyArticle
    131 N2592ad0ee29d412e9b52903bc04d7399 schema:issueNumber 3
    132 rdf:type schema:PublicationIssue
    133 N27b2a977b1674604af3b7f0213afaafd rdf:first sg:person.016152757367.11
    134 rdf:rest Ncae433d5bc384f1a911e86bc1b73174f
    135 N44fbc1d48f014c5d9afcacd169db567a schema:name dimensions_id
    136 schema:value pub.1147256097
    137 rdf:type schema:PropertyValue
    138 N62c04a5192514a9aa900059dd5bf417a rdf:first sg:person.012633261257.13
    139 rdf:rest N27b2a977b1674604af3b7f0213afaafd
    140 Nb1f77f21dbce4bb481f99fdafe8c4c40 schema:name doi
    141 schema:value 10.1007/s41064-022-00206-y
    142 rdf:type schema:PropertyValue
    143 Ncae433d5bc384f1a911e86bc1b73174f rdf:first sg:person.012321336324.12
    144 rdf:rest rdf:nil
    145 Nd1312ed5044546839295992054e458e7 rdf:first sg:person.010155657025.69
    146 rdf:rest N62c04a5192514a9aa900059dd5bf417a
    147 Nd53edb8ef1684e7187c52ce3be5a3080 schema:name Springer Nature - SN SciGraph project
    148 rdf:type schema:Organization
    149 Nddd8d7eecb5f41c195d227a5be89cccc schema:volumeNumber 90
    150 rdf:type schema:PublicationVolume
    151 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    152 schema:name Information and Computing Sciences
    153 rdf:type schema:DefinedTerm
    154 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    155 schema:name Artificial Intelligence and Image Processing
    156 rdf:type schema:DefinedTerm
    157 sg:grant.8101872 http://pending.schema.org/fundedItem sg:pub.10.1007/s41064-022-00206-y
    158 rdf:type schema:MonetaryGrant
    159 sg:journal.1320463 schema:issn 2512-2789
    160 2512-2819
    161 schema:name PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science
    162 schema:publisher Springer Nature
    163 rdf:type schema:Periodical
    164 sg:person.010155657025.69 schema:affiliation grid-institutes:grid.15649.3f
    165 schema:familyName Song
    166 schema:givenName Yifan
    167 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010155657025.69
    168 rdf:type schema:Person
    169 sg:person.012321336324.12 schema:affiliation grid-institutes:grid.15649.3f
    170 schema:familyName Köser
    171 schema:givenName Kevin
    172 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012321336324.12
    173 rdf:type schema:Person
    174 sg:person.012633261257.13 schema:affiliation grid-institutes:grid.15649.3f
    175 schema:familyName Nakath
    176 schema:givenName David
    177 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012633261257.13
    178 rdf:type schema:Person
    179 sg:person.016152757367.11 schema:affiliation grid-institutes:grid.15649.3f
    180 schema:familyName She
    181 schema:givenName Mengkun
    182 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016152757367.11
    183 rdf:type schema:Person
    184 sg:pub.10.1007/11550518_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010438544
    185 https://doi.org/10.1007/11550518_6
    186 rdf:type schema:CreativeWork
    187 sg:pub.10.1007/11585978_5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046787860
    188 https://doi.org/10.1007/11585978_5
    189 rdf:type schema:CreativeWork
    190 sg:pub.10.1007/3-540-45053-x_42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018932029
    191 https://doi.org/10.1007/3-540-45053-x_42
    192 rdf:type schema:CreativeWork
    193 sg:pub.10.1007/978-3-030-05792-3_7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110765748
    194 https://doi.org/10.1007/978-3-030-05792-3_7
    195 rdf:type schema:CreativeWork
    196 sg:pub.10.1007/978-3-030-30683-0_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1122015579
    197 https://doi.org/10.1007/978-3-030-30683-0_11
    198 rdf:type schema:CreativeWork
    199 sg:pub.10.1007/978-3-030-33676-9_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1122090658
    200 https://doi.org/10.1007/978-3-030-33676-9_6
    201 rdf:type schema:CreativeWork
    202 sg:pub.10.1007/978-3-030-68790-8_29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1135654073
    203 https://doi.org/10.1007/978-3-030-68790-8_29
    204 rdf:type schema:CreativeWork
    205 sg:pub.10.1007/978-3-030-68790-8_30 schema:sameAs https://app.dimensions.ai/details/publication/pub.1135653521
    206 https://doi.org/10.1007/978-3-030-68790-8_30
    207 rdf:type schema:CreativeWork
    208 sg:pub.10.1007/978-3-319-16649-0_14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008050171
    209 https://doi.org/10.1007/978-3-319-16649-0_14
    210 rdf:type schema:CreativeWork
    211 sg:pub.10.1007/978-3-319-27702-8_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038546534
    212 https://doi.org/10.1007/978-3-319-27702-8_4
    213 rdf:type schema:CreativeWork
    214 sg:pub.10.1007/978-3-642-19309-5_39 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047331563
    215 https://doi.org/10.1007/978-3-642-19309-5_39
    216 rdf:type schema:CreativeWork
    217 sg:pub.10.1007/978-3-642-19315-6_3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009780475
    218 https://doi.org/10.1007/978-3-642-19315-6_3
    219 rdf:type schema:CreativeWork
    220 sg:pub.10.1007/978-3-642-33715-4_61 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014004696
    221 https://doi.org/10.1007/978-3-642-33715-4_61
    222 rdf:type schema:CreativeWork
    223 sg:pub.10.1007/bf00127171 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040466580
    224 https://doi.org/10.1007/bf00127171
    225 rdf:type schema:CreativeWork
    226 sg:pub.10.1007/s00371-012-0679-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1051502328
    227 https://doi.org/10.1007/s00371-012-0679-y
    228 rdf:type schema:CreativeWork
    229 sg:pub.10.1007/s10661-006-9239-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016059929
    230 https://doi.org/10.1007/s10661-006-9239-0
    231 rdf:type schema:CreativeWork
    232 sg:pub.10.1007/s11042-020-10049-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1132207931
    233 https://doi.org/10.1007/s11042-020-10049-7
    234 rdf:type schema:CreativeWork
    235 sg:pub.10.1007/s11263-006-0002-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049205751
    236 https://doi.org/10.1007/s11263-006-0002-3
    237 rdf:type schema:CreativeWork
    238 sg:pub.10.1023/a:1016328200723 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010390647
    239 https://doi.org/10.1023/a:1016328200723
    240 rdf:type schema:CreativeWork
    241 sg:pub.10.1023/a:1022314423998 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005564593
    242 https://doi.org/10.1023/a:1022314423998
    243 rdf:type schema:CreativeWork
    244 sg:pub.10.1023/b:ssta.0000018445.25977.f3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019662077
    245 https://doi.org/10.1023/b:ssta.0000018445.25977.f3
    246 rdf:type schema:CreativeWork
    247 sg:pub.10.1038/s41598-017-09382-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1091255960
    248 https://doi.org/10.1038/s41598-017-09382-z
    249 rdf:type schema:CreativeWork
    250 sg:pub.10.1038/s41598-019-44492-w schema:sameAs https://app.dimensions.ai/details/publication/pub.1115994617
    251 https://doi.org/10.1038/s41598-019-44492-w
    252 rdf:type schema:CreativeWork
    253 sg:pub.10.1038/scientificamerican1277-108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056644898
    254 https://doi.org/10.1038/scientificamerican1277-108
    255 rdf:type schema:CreativeWork
    256 grid-institutes:grid.15649.3f schema:alternateName Oceanic Machine Vision, GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148, Kiel, Germany
    257 schema:name Oceanic Machine Vision, GEOMAR Helmholtz Centre for Ocean Research Kiel, 24148, Kiel, Germany
    258 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...