A comprehensive qualitative and quantitative survey on image dehazing based on deep neural networks

Abstract

Image dehazing has become a necessary area of research with the increasing popularity and demand of computer vision systems. Image dehazing is a method to remove haze from an image to improve its visual quality. Dehazing techniques are widely employed in a variety of computer vision applications to enhance their overall performance. Many techniques have been proposed by researchers in recent years to eliminate the haze from an image. However, there is a lack of available literature that provides a summary of deep learning-based state-of-the-art image dehazing methods. In this study, we provide a detailed review of recently proposed image dehazing techniques based on deep neural networks such as CNN, GAN, RNN, RCNN, and Transformer. A concise review of significant applications of image dehazing, benchmark datasets, and various performance metrics are also presented. We compare the state-of-the-art methods quantitatively using performance evaluation metrics such as SSIM and PSNR. Finally, this study discusses the fundamental difficulties associated with image dehazing approaches that need to be further explored.