02857nas a2200265   4500000000100000000000100001008004100002260001200043653001900055653002000074653002900094100003000123700001900153700002400172700003100196700002400227700002000251700003300271245012800304856008200432300001200514490000600526520204500532022001402577       2021                            d  c12/202110aImage Dehazing10aImage Defogging10aImage Quality Assessment1 aKarrar Hameed Abdulkareem1 aNureize Arbaiy1 aZainab Hussein Arif1 aMohammed Nasser Al-Mhiqani1 aMazin Abed Mohammed1 aSeifedine Kadry1 aZaid Abdi Alkareem Alyasseri00aMapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations  uhttps://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_16_0.pdf  a172-1980 v73 aOur study aims to review and analyze the most relevant studies in the image dehazing field. Many aspects have been deemed necessary to provide a broad understanding of various studies that have been examined through surveying the existing literature. These aspects are as follows: datasets that have been used in the literature, challenges that other researchers have faced, motivations, and recommendations for diminishing the obstacles in the reported literature. A systematic protocol is employed to search all relevant articles on image dehazing, with variations in keywords, in addition to searching for evaluation and benchmark studies. The search process is established on three online databases, namely, IEEE Xplore, Web of Science (WOS), and ScienceDirect (SD), from 2008 to 2021. These indices are selected because they are sufficient in terms of coverage. Along with definition of the inclusion and exclusion criteria, we include 152 articles to the final set. A total of 55 out of 152 articles focused on various studies that conducted image dehazing, and 13 out 152 studies covered most of the review papers based on scenarios and general overviews. Finally, most of the included articles centered on the development of image dehazing algorithms based on real-time scenario (84/152) articles. Image dehazing removes unwanted visual effects and is often considered an image enhancement technique, which requires a fully automated algorithm to work under real-time outdoor applications, a reliable evaluation method, and datasets based on different weather conditions. Many relevant studies have been conducted to meet these critical requirements. We conducted objective image quality assessment experimental comparison of various image dehazing algorithms. In conclusions unlike other review papers, our study distinctly reflects different observations on image dehazing areas. We believe that the result of this study can serve as a useful guideline for practitioners who are looking for a comprehensive view on image dehazing.  a1989-1660