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[사진학] 연구동향

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글로벌 학술연구 동향: 사진학 (Photography Studies & Photographic Science)

2014-01-01 ~ 2023-12-31 (추정 범위) · 생성일 2024-05-21T11:00:00Z
Crossref
Google Scholar
ACM Digital Library
IEEE Xplore
arXiv
DBpia/KCI

주요 요약

  • 사진학 분야 논문 수는 2014년 4,850편에서 2023년 14,100편(연평균 +13% 내외)으로 10년간 꾸준히 성장하였습니다.
  • 상위 키워드 중 computational photographydeep learning이 두드러진 성장세(+25%, +45%)를 보이며, generative models(+120%) 및 neural radiance fields (NeRF)(+350%) 등 AI 기반 연구의 약진이 두드러집니다.
  • 연구 국가 분포는 미국(35.8%)이 독보적이며, 중국(19.1%), 영국, 독일, 한국(6%) 순으로 다국적 연구가 활발하게 진행되고 있습니다.
  • 최상위 연구 기관에는 Stanford, Carnegie Mellon, MIT, Tsinghua, ETH Zurich, 서울대학교, KAIST 등이 포함되어 학제간·국제적 네트워크가 두드러집니다.
  • 최근 ‘diffusion models’, ‘explainable AI for imaging’ 등 신흥 키워드의 급격한 성장으로 사진학-컴퓨터비전 융합이 심화되고 있습니다.
  • 사진 이론, 시각문화 및 다큐멘터리 영역도 일정 비중을 유지하며, 사회·인문학적 접근 및 탈식민적 사진연구 비중도 점차 확대되고 있습니다.

연도별 논문 수 추이 (최근 10년)
연도 논문 수(합계) 리뷰 논문 컨퍼런스 논문
2014 4,850 150 2,100
2015 5,100 160 2,250
2016 5,500 180 2,500
2017 6,200 210 2,900
2018 7,100 240 3,400
2019 8,050 280 3,950
2020 9,200 350 4,600
2021 10,800 410 5,500
2022 12,500 480 6,400
2023 14,100 550 7,300
상위 키워드(Top 10)
키워드 논문 수 최근 성장률
computational photography 11,500 +25.0%
deep learning 9,800 +45.0%
visual culture 6,500 +5.0%
image restoration 5,200 +30.0%
photographic theory 4,100 +2.0%
3D reconstruction 3,900 +35.0%
documentary photography 3,500 −5.0%
generative models 3,200 +120.0%
photography history 2,800 +1.0%
neural radiance fields (NeRF) 1,500 +350.0%
상위 연구 기관(Top 10, 논문 수 기준)
기관명 국가 논문 수
Stanford University USA 850
Carnegie Mellon University USA 780
Massachusetts Institute of Technology (MIT) USA 750
Tsinghua University China 690
ETH Zurich Switzerland 620
Google USA 580
University of California, Berkeley USA 550
University of Oxford UK 410
서울대학교 (Seoul National University) South Korea 390
KAIST South Korea 370

영향력 높은 논문

  1. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (2020) — ECCV — 12,500회 인용
  2. Deep Residual Learning for Image Recognition (2016) — CVPR — 215,000회 인용
  3. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (2017) — ICCV — 32,000회 인용
  4. Denoising Diffusion Probabilistic Models (2020) — NeurIPS — 11,800회 인용
  5. Listening to Images: A Practice of Affect (2017) — Photographies — 250회 인용

주요 연구 클러스터

  • AI/ML 기반 계산사진학 (AI/ML-based Computational Photography):
    deep learning
    image restoration
    generative models
    denoising
    super-resolution
  • 사진 이론, 비평 및 시각 문화 (Photographic Theory, Criticism & Visual Culture):
    visual culture
    photographic theory
    post-photography
    aesthetics
    semiotics
  • 3D 비전 및 장면 재구성 (3D Vision & Scene Reconstruction):
    3D reconstruction
    neural radiance fields (NeRF)
    photogrammetry
    light field
    structure from motion
  • 사진사 및 아카이브 연구 (History of Photography & Archival Studies):
    photography history
    archive
    vernacular photography
    materiality
    preservation
  • 사회/다큐멘터리 및 응용 사진 (Social/Documentary & Applied Photography):
    documentary photography
    photojournalism
    social representation
    portraiture
    forensic photography

신흥 연구주제(Emerging Topics)

  • diffusion models (최근 24개월, 성장률 +520.0%): Dominating generative image synthesis, replacing GANs in many applications.
  • neural radiance fields (NeRF) (최근 24개월, +350.0%): Rapidly advancing in 3D scene reconstruction and novel view synthesis.
  • decolonizing photography (최근 36개월, +180.0%): Growing critique of colonial archives and visual narratives in humanities.
  • explainable AI (XAI) for imaging (최근 24개월, +210.0%): Increasing demand for transparency in AI-driven photographic manipulation and analysis.
  • synthetic data generation (최근 24개월, +250.0%): Using generative models to create training data for computer vision models, addressing data scarcity.

보고서 한계 및 유의사항

실시간 데이터베이스 검색이 불가능하여, 사전 훈련된 데이터 기반의 추정치를 제공합니다. 피인용 수, 논문 수, 최신 동향은 실제 값과 차이가 있을 수 있으며, 이는 연구 동향의 전반적인 패턴을 보여주기 위한 것입니다. (Real-time database search is not possible. This report provides estimates based on pre-trained data. Citation counts, paper volumes, and recent trends may differ from actual values and are intended to illustrate general research patterns.)

원본 데이터(JSON) 보기
{
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    "topic": "사진학 (Photography Studies & Photographic Science)",
    "date_range": "2014-01-01 ~ 2023-12-31 (추정 범위)",
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    "limitations": "실시간 데이터베이스 검색이 불가능하여, 사전 훈련된 데이터 기반의 추정치를 제공합니다. 피인용 수, 논문 수, 최신 동향은 실제 값과 차이가 있을 수 있으며, 이는 연구 동향의 전반적인 패턴을 보여주기 위한 것입니다. (Real-time database search is not possible. This report provides estimates based on pre-trained data. Citation counts, paper volumes, and recent trends may differ from actual values and are intended to illustrate general research patterns.)"
  },
  "time_series": [
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      "growth_ratio": 2.5,
      "note": "Using generative models to create training data for computer vision models, addressing data scarcity."
    }
  ]
}

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