IMAGE DEHAZING USING FAST ITERATIVE DOMAIN GUIDED IMAGE FILTERING WITH GRAY WORLD OPTIMIZATION

Abstract

When remote sensing photos are taken, they are often captured in hazy circumstances such as fog, snow, thin clouds, dust, and other similar situations, which causes the contrast in the image to decrease. The term "dehazing" refers to the process of removing haze or other atmospheric pollutants from a photograph. The goal of this approach is to improve the overall quality of the image and to make it more appealing to the customer. However, the majority of the procedures that are considered to be state of the art were not successful in completely removing the atmospheric effects from the picture. This article is primarily focused on the construction of a gray world optimization (GWO) algorithm for the purpose of providing an accurate assessment of ambient light. This is done in order to find a solution to the issue. A unique approach for dark channel prior-based transmission map estimation and refining is also developed as part of this study. This method is applied in a pixel-wise and patch-wise way. Because of this, the atmospheric effects are resolved in each and every patch that is based on pixels. In conclusion, a rapid iterative domain guided image filtering (ID-GIF) technique was created in order to achieve the goal of obtaining smoother output with dehazing qualities. Compared to the methodologies that are considered to be state of the art, the findings of the simulation demonstrate that the work that is being suggested offers superior quantitative and qualitative outcomes.