Published Paper Details:

In recent days many organisations or firms have made their process digitalized, so as such in such a kind of globalised generation a lot of data is being transferred in and around among which much of them are based on images or videos etc. For example let�s take the present scenario of the space rovers which take the snaps of the orbital atmosphere and reflect the data of images to the earth, so in such a scenario image processing comes into existence. Outdoor images are used in a vast number of applications, such as surveillance, remote sensing, and autonomous navigation. The greatest issue with these types of images is the effect of environmental pollution: haze, smog, and fog originating from suspended particles in the air, such as dust, carbon, and water drops, which cause degradation, is essential for the input of computer vision systems. Most of the state-of-the-art research in de-hazing algorithms is focused on improving the estimation of transmission maps, which are also known as depth maps. Transmission maps are essential because they directly explain the quality of the image restoration. In this page an exclusive algorithm and morphological operations are proposed which evicts all the hazy layers of the image and restore the prior fine quality of the image which is purely done based on the usage colour �Dark-Channel-Prior�. The obtained experimental results are evaluated and compared qualitatively and quantitatively with other de-hazing algorithms using the metrics of the peak signal-to-noise ratio and structural similarity index; based on these metrics, it is found that the proposed algorithm has improved performance compared with recently introduced approaches.