Innovative Image Dehazing Techniques Using Machine Learning Models

Image dehazing is a significant task in computer vision, which aims to restore the visibility of images affected by atmospheric particles like fog, haze, and smoke. Traditional model-based dehazing approaches usually depend on prior assumptions for atmospheric light and transmission maps resulting in unsatisfactory estimates for complex scenes. The deep learning techniques particularly the Convolution Neural Networks (CNNs) led to the development of robust and adaptive solutions for image dehazing. This paper provides a survey of CNN based dehazing methods, such as residual networks, a ranking CNN, and multi-scale network. It is based on deep feature extraction and learning-based transmission estimation that can eliminate haze and enhance images. Comparative analyses show that CNN-based methods outperform traditional dehazing approaches in terms of PSNR, SSIM, and visual quality. Experimental results show that deep learning-based dehazing methods are able to effectively dehaze images over various datasets, and such abilities are of great interest for applications in surveillance, autonomous driving, and remote sensing.