Aethra-net: Single image and video dehazing using autoencoder

Abstract

A fast and efficient video dehazing system with low computational complexity has a huge demand among drivers during hazy winter nights. There are only a few video dehazing models that exist in literature. Video dehazing requires the sequential extraction and processing of frames. The processed frames must be restored in the same sequence as the original video. However, the existing video dehazing algorithms suffer from color distortion due to the continuous processing of frames. They are not suitable for videos with dense haze. Furthermore, some dehazing systems require hardware, whereas the proposed model is completely software-based to reduce the computational costs. In this paper, an image and video dehazing system called Aethra-Net is developed. A gush enhancer-based autoencoder is modified to obtain the transmission map. The structure of gush enhancement module resembles the processing of light entering the human eye from different paths. The multiple blocks of Resnet-101 layers are employed to overcome vanishing gradient problem. The vessel enhancement filter is also incorporated to enhance the performance of the proposed system. The proposed model has a susceptibility to compute the dehazed images effectively. The proposed model is evaluated on various benchmark datasets and compared with the existing dehazing techniques. Experimental results reveal that the performance of Aethra-Net is found superior as compared to the existing dehazing models.