In the presence of fog, photographs often suffer from poor visibility due to
the atmospheric conditions. This reduction in visibility stems from the obscuring effect
of fog on the surrounding environment. Additionally, the presence of particles, dust,
and pollutants further contributes to color dilution and diminished contrast in analyzed
images, an inevitable consequence of these contaminants. As this phenomenon persists,
distinctions between individuals may gradually blur in their collective consciousness.
Within the context of this investigation, a novel method is proposed for single-image
processing, leveraging a hybrid filter. Specifically, it suggests the adoption of GGIF
(Globally Guided Image Filtering) in conjunction with smoothening filters such as
Weighted Least Squares (WLS) and Two-Dimensional Bilateral Filters. The
experimental results demonstrate enhanced haze removal and denoising across all
images, showcasing the method's efficacy with an overall perceptual fog density (PFD)
reduction improvement of 55.39% and the flexibility to adjust parameters, including
the sigma variable in GGIF, for improved performance
Keywords: Dehazing, Denoising, Globally Guided Image Filtering (GGIF), Twodimensional bilateral filter, Weighted least squares.
