Comprehensive Review of Machine Learning (ML) in Image Defogging: Taxonomy of Concepts, Scenes, Feature Extraction, and Classification techniques-Reference-Cited by-同舟云学术

Comprehensive Review of Machine Learning (ML) in Image Defogging: Taxonomy of Concepts, Scenes, Feature Extraction, and Classification techniques

Published:2021-11-23 Issue:2 Volume:16 Page:289-310
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET Image Processing

Author:

Arif Zainab Hussein¹²,Mahmoud Moamin A.¹,Abdulkareem Karrar Hameed³^ORCID,Mohammed Mazin Abed⁴^ORCID,Al‐Mhiqani Mohammed Nasser⁵,Mutlag Ammar Awad⁶,Damaševičius Robertas⁷^ORCID

Affiliation:

1. College of Computing and Informatics Universiti Tenaga Nasional Selangor Malaysia

2. College of Computer Science and Information Technology University of Al‐Qadisiyah Diwaniyah Iraq

3. College of Agriculture Al‐Muthanna University Samawah Iraq

4. College of Computer Science and Information Technology University of Anbar Anbar Iraq

5. Information Security and Networking Research Group (InFORSNET) Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Melaka Durian Tunggal Malaysia

6. Pure Science Department Ministry of Education/General Directorate of Curricula Baghdad Iraq

7. Faculty of Applied Mathematics Silesian University of Technology Gliwice Poland

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12365

Reference138 articles.

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2. A Review on Different Image De-hazing Methods

3. Review of Video and Image Defogging Algorithms and Related Studies on Image Restoration and Enhancement

4. Recent advances in image dehazing

5. Tang K. Yang J. Wang J.:In investigating haze‐relevant features in a learning framework for image dehazing. In:2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 2995–3002(2014)

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