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 Visibility Restoration: A Systematic Review and Meta-Analysis
Tác giả hoặc Nhóm tác giả: Dat Ngo, Seungmin Lee, Tri Minh Ngo, Gi-Dong Lee and Bongsoon Kang
Nơi đăng: Sensors; Số: No. 8: 2625;Từ->đến trang: 1-41;Năm: 2021
Lĩnh vực: Khoa học công nghệ; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
Image acquisition is a complex process that is affected by a wide variety of internal andenvironmental factors. Hence, visibility restoration is crucial for many high-level applications inphotography and computer vision. This paper provides a systematic review and meta-analysis ofvisibility restoration algorithms with a focus on those that are pertinent to poor weather conditions. This paper starts with an introduction to optical image formation and then provides a comprehensive description of existing algorithms as well as a comparative evaluation. Subsequently, there is a thorough discussion on current difficulties that are worthy of a scientific effort. Moreover, this paper proposes a general framework for visibility restoration in hazy weather conditions while using haze-relevant features and maximum likelihood estimates. Finally, a discussion on the findings and future developments concludes this paper.
ABSTRACT
Image acquisition is a complex process that is affected by a wide variety of internal andenvironmental factors. Hence, visibility restoration is crucial for many high-level applications inphotography and computer vision. This paper provides a systematic review and meta-analysis ofvisibility restoration algorithms with a focus on those that are pertinent to poor weather conditions. This paper starts with an introduction to optical image formation and then provides a comprehensive description of existing algorithms as well as a comparative evaluation. Subsequently, there is a thorough discussion on current difficulties that are worthy of a scientific effort. Moreover, this paper proposes a general framework for visibility restoration in hazy weather conditions while using haze-relevant features and maximum likelihood estimates. Finally, a discussion on the findings and future developments concludes this paper.
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