Abstract
The method of single image-based dehazing is addressed in the last two decades due to its extreme variating properties in different environments. Different factors make the image dehazing process cumbersome like unbalanced airlight, contrast, and darkness in hazy images. Many estimating and learning-based techniques are used to dehaze the images to overcome the aforementioned problems that suffer from halo artifacts and weak edges. The proposed technique can preserve better edges and illumination and retain the original color of the image. Dark channel prior (DCP) and probability-weighted moments (PWMs) are applied on each channel of an image to suppress the hazy regions and enhance the true edges. PWM is very effective as it suppresses low variations present in images that are affected by the haze. We have proposed a method in this article that performs well as compared to state-of-the-art image dehazing techniques in various conditions which include illumination changes, contrast variation, and preserving edges without producing halo effects within the image. The qualitative and quantitative analysis carried on standard image databases proves its robustness in terms of the standard performance evaluation metrics.
Highlights
Natural outdoor images and their perception is a key factor in image understanding
Performance Comparisons and Discussions. e proposed method based on the dark channel probability-weighted moments (DCPWMs) outperforms both qualitatively and quantitatively as compared to 7 state-ofthe-art image dehazing methods
According to the experimental results, the DCPWM method outperforms as compared with its competitor image dehazing methods in terms of illumination, natural color, edges, and original clear sky. e original hazy image is presented in the first column of Figure 7(a) with 9 different images used for qualitative comparison
Summary
Rehan Mehmood Yousaf ,1 Hafiz Adnan Habib, Zahid Mehmood ,2 Ameen Banjar, Riad Alharbey ,3 and Omar Aboulola. E method of single image-based dehazing is addressed in the last two decades due to its extreme variating properties in different environments. Many estimating and learning-based techniques are used to dehaze the images to overcome the aforementioned problems that suffer from halo artifacts and weak edges. E proposed technique can preserve better edges and illumination and retain the original color of the image. Dark channel prior (DCP) and probability-weighted moments (PWMs) are applied on each channel of an image to suppress the hazy regions and enhance the true edges. We have proposed a method in this article that performs well as compared to stateof-the-art image dehazing techniques in various conditions which include illumination changes, contrast variation, and preserving edges without producing halo effects within the image.
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