The normalized random map of gradient for generating multifocus image fusion

The multifocus image fusion is a kind of method in image processing to collecting the sharp information from multifocus image sequence. This method is purposed to simplify the reader understand the complex information of image sequence in an image only. There are many methods to generate fused image from several images so far. Many researchers have developed many new and sophisticated methods. They show complicated computation and algorithm. So, that it is difficult to understand by the new students or viewer. Furthermore, they get difficulties to create the new one. In order to handle this problem, the proposed method a concise algorithm which is able to generate an accurate fused image without using a complicated mathematical equation and tough algorithm. The proposed method is the normalized random map of gradient for generating multifocus image fusion. By generate random map of gradient, the algorithm is able to specify the coarse focus region accurately. The random map of gradient is a kind of information formed independently from independent matrix. This data has a significant role in predict the initial focus regions. The proposed algorithm successes to supersede difficulties of mathematical equations and algorithms. It successes to eliminate the mathematical and algorithm problems. Furthermore, the evaluation of proposed method based on the fused image quality. The Mutual Information and Structure Similarity Indexes become our key parameter assessment. The results show that the outputs have high indexes. It means it is acceptable. Then the implementation of multifocus image fusion will increase the quality of the applied fields such as remote sensing, robotics, medical diagnostics and so on. It is also possible implemented in other new fields

Mapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations

Our 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