Enhancing the Unseen: Deep Learning Architectures for Robust Image Quality Improvement in Severe Weather

The main objective of this research study is the development and design of an innovative deep learning-based framework to enhance image in adverse weather conditions to enhance the quality and visibility of the image and the fineness of the details in a variety of settings and uses. In snow, rain, and fog, the works through the precipitation, of dispersing light to widely angled sides, which creates the dangers of death and accidents. The deep-learning-based architecture is required to provide enhanced imaging power in low-visibility backgrounds. To enhance the quality of the pictures, this model utilised techniques such as generative adversarial networks (GANs), convolutional neural network (CNN) models, edge-optimised YOLO tea, and ViT-Tea vision transformer to solve the issue of low light, snow, rain, and fog weather conditions. The literature review suggests a number of deep-learning models helpful in improving the details of the images in the case of untimely adverse weather conditions, and helps in improving the performance of the real situations in terms of performance and visibility. The research highlights the significant benefits of deep-learning techniques, which are needed for increasing image quality in unfavourable environments such as fog, haze and smoke. It plays a critical role in several industries such as satellite imaging, intelligence, transportation, agriculture and outdoor surveillance.