A Review on Efficient state-of-the-art Image Dehazing Techniques
R.Prakash Kumar, N Manja Naik · 2022
Image dehazing is a rapid research area in the field of video surveillance and computer applications. Images are degraded by the scattering of atmospheric particles in the light, fog, and air particles in the atmosphere. In this paper, we are discussing conventional existing techniques such as the Dark Channel Prior method (DCP), Median Dark Channel Prior Method (MDCP), and Average Image Dehazing Model, but these methods suffer from a lot of computing complexity and even visual distortions like halos and over-saturation. To reduce the computational complexity and recover the original image from the hazy image Deep Learning and Convolutional Neural networks (CNN) are used. In this paper, we discussed various methods to reduce the haze in an image by using a feature/learning-based Convolutional Neural Network (CNN) such as Attention-based Transmission Estimation and Classification Fusion Network (ATECFN), and Encoder-Decoder architecture.