A Polarization Image Dehazing Method Based on the Principle of Physical Diffusion

TL;DR

This paper introduces a semi-physical polarization dehazing technique that leverages the properties of polarized light and models fog diffusion to improve image clarity in foggy environments without needing external light sources.

Contribution

It presents a novel semi-physical method that simulates fog diffusion and uses Fourier transforms and deconvolution to enhance foggy images based on polarization principles.

Findings

Effective dehazing in foggy scenes

Improved image detail preservation

No external light source required

Abstract

Computer vision is increasingly used in areas such as unmanned vehicles, surveillance systems and remote sensing. However, in foggy scenarios, image degradation leads to loss of target details, which seriously affects the accuracy and effectiveness of these vision tasks. Polarized light, due to the fact that its electromagnetic waves vibrate in a specific direction, is able to resist scattering and refraction effects in complex media more effectively compared to unpolarized light. As a result, polarized light has a greater ability to maintain its polarization characteristics in complex transmission media and under long-distance imaging conditions. This property makes polarized imaging especially suitable for complex scenes such as outdoor and underwater, especially in foggy environments, where higher quality images can be obtained. Based on this advantage, we propose an innovative semi-physical polarization dehazing method that does not rely on an external light source. The method simulates the diffusion process of fog and designs a diffusion kernel that corresponds to the image blurriness caused by this diffusion. By employing spatiotemporal Fourier transforms and deconvolution operations, the method recovers the state of fog droplets prior to diffusion and the light inversion distribution of objects. This approach effectively achieves dehazing and detail enhancement of the scene.