Automated Cloud Removal on High-Altitude UAV Imagery Through Deep Learning on Synthetic Data

New theories and applications of deep learning have been discovered and implemented within the field of machine learning recently. The high degree of effectiveness of deep learning models span across many domains including image processing and enhancement. Specifically, the automated removal of clouds, smoke, and haze from images has become a prominent and pertinent field of research. In this paper, I propose an analysis and synthetic training data variant for the All-in-One Dehazing Network (AOD-Net) architecture that performs better on removing clouds and haze; most specifically on high altitude unmanned aerial vehicles (UAVs) images

Fast Video Dehazing Using Per-Pixel Minimum Adjustment

To reduce the computational complexity and maintain the effect of video dehazing, a fast and accurate video dehazing method is presented. The preliminary transmission map is estimated by the minimum channel of each pixel. An adjustment parameter is designed to fix the transmission map to reduce color distortion in the sky area. We propose a new quad-tree method to estimate the atmospheric light. In video dehazing stage, we keep the atmospheric light unchanged in the same scene by a simple but efficient parameter, which describes the similarity of the interframe image content. By using this method, unexpected flickers are effectively eliminated. Experiments results show that the proposed algorithm greatly improved the efficiency of video dehazing and avoided halos and block effect

Automated Cloud Removal on High-Altitude UAV Imagery Through Deep Learning on Synthetic Data

New theories and applications of deep learning have been discovered and implemented within the field of machine learning recently. The high degree of effectiveness of deep learning models span across many domains including image processing and enhancement. Specifically, the automated removal of clouds, smoke, and haze from images has become a prominent and pertinent field of research. In this paper, I propose an analysis and synthetic training data variant for the All-in-One Dehazing Network (AOD-Net) architecture that performs better on removing clouds and haze; most specifically on high altitude unmanned aerial vehicles (UAVs) images

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