A cross layer graphical neural network based convolutional neural network framework for image dehazing

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

Acknowledgement

There is no acknowledgement involved in this work.

Authorship contributions

All authors contributed equally to this work.

Disclosure statement

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No participation of humans takes place in this implementation process.

Human and animal rights

No violation of Human and Animal Rights is involved.

Data availability statement

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.