Restoring high-quality images from degraded hazy observations is a fundamental and
essential task in the field of computer vision. While deep models have achieved significant …

Image dedusting has gained increasing attention as a preprocessing step for computer
vision tasks. Current traditional image dedusting methods rely on a variety of constraints or …

This paper presents a deep learning method for image dehazing and clarification. The main
advantages of the method are high computational speed and using unpaired image data for …

Scattering and attenuation of light in no-homogeneous imaging media or inconsistent light
intensity will cause insufficient contrast and color distortion in the collected images, which …

When applying deep learning and image processing techniques for bridge crack detection,
the obtained images in real-world scenarios have severe image degradation problem. This …

Deep learning (DL) has recently shown superior performance for single-image dehazing.
Most deep dehazing networks either estimate the parameters of the atmospheric scattering …

In real-world scenarios, the haze presents diversity and complexity. However, current
dehazing researches usually focus solely on specific categories or the removal of common …

Vision gets obscured in adverse weather conditions, such as heavy downpours, dense fog,
haze, snowfall, etc., which increase the number of road accidents yearly. Modern …

We proposed a novel image-enhancing framework to ensure consolidated restoration
accuracy when remedying the visual quality of dehazed images, such as over-saturation …

High-quality imaging is essential for effective safety supervision and intelligent deployment
in vision-based measurement systems (VMS). It allows for accurate and comprehensive …