Foggy image processing: enhancement and reconstruction approaches

A foggy environment is a critical hurdle for computer vision systems, which negatively affects their efficacy in applications like self-driving car technology, inspection, and sensing. This paper provides an in-depth review of current methodologies aimed at resolving the challenges presented by foggy conditions, focusing on image enhancement and reconstruction techniques. We examine a diverse range of solutions, starting from traditional approaches like histogram equalization and filtering to advanced techniques utilizing deep learning, which incorporates convolutional neural networks and transformers. The study discusses the merits and limits of each approach, emphasizing their ability to improve visibility, contrast, and color fidelity in foggy scenarios. We also discuss the applications of these methods across various fields and their societal implications. The paper gives a current evaluation of key achievements in the area and identifies objectives for future study aimed at boosting the dependability and efficacy of computer vision systems in hazy situations.