Enhanced Feature Extraction for Image Dehazing: A Comparative Study between Deep Learning Architectures and FFA-NET

摘要

The problem of poor visibility in foggy images has spurred various image de-hazing strategies. As the need for high-quality images grows, especially for autonomous systems, this research aims to leverage different Deep Learning (DL) architectures to draw out key details from images , localizing this retrieved data to mitigate the impact of haze. The work explores using DL methods, particularly contrasting the regression and classification models of Convolutional Neural Networks (CNN), to remove haze from foggy images. This work sets the stage for further developments in image processing, particularly in conditions with poor visibility. It opens opportunities for improving image quality in various applications, such as autonomous driving and outdoor robotics, where clarity of vision is crucial. The final stage of the proposed model involves three specific pre-processing methods: contextual regularization, air light estimation and boundary constraint for optimal results. The next stage sets out to determine the best DL model for producing clear images from de-hazed ones.