Original scientific paper
https://doi.org/10.1080/00051144.2024.2346964
A cross layer graphical neural network based convolutional neural network framework for image dehazing
M. Pavethra
; Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
*
M. Uma Devi
; Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
* Corresponding author.
Abstract
The current version of imaging equipment cannot quickly and effectively make up for the reduction of visibility triggered by bad weather. Traditional strategies minimize hazy impacts by
employing an image depth model and a physical model. Following experts, erroneous depth
data reduces the efficacy of the dehazing algorithm. Dehazing methods based on CNN are
imperfect to handle region which is bright or similar to atmospheric light and thus leads to
oversaturation of pixels. These challenges can be addressed by proposing a novel model that
incorporates the idea of a Graphical Neural Network. The amount of light coming from the atmosphere is estimated using normalization where the contrast of the image gets adjusted using
Bias Contrast stretch Histogram Equalization. An enhanced Transmission map estimator is used
to render the hazy scene. Finally, the cross-layer graphical neural network-based CNN model is
applied to produce a haze-free image and eliminate the over-saturation of pixels. Extensive evaluation findings show that the proposed approach can significantly recuperate misty imagery,
even if the images have a substantial amount of haze.
Keywords
Image dehazing; Bias Contrast Stretch Histogram Equalization; enhanced transmission map; cross-layer graphical neural network
Hrčak ID:
326268
URI
Publication date:
9.5.2024.
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