Abstract
Atmospheric scattering caused by suspended particles in the air severely degrades the scene radiance. This paper proposes a method to remove haze by using a neural network that combines scene polarization information. The neural network is self-supervised and online globally optimization can be achieved by using the atmospheric transmission model and gradient descent. Therefore, the proposed method does not require any haze-free image as the constraint for neural network training. The proposed approach is far superior to supervised algorithms in the performance of dehazing and is highly robust to the scene. It is proved that this method can significantly improve the contrast of the original image, and the detailed information of the scene can be effectively enhanced.
Highlights
The existence of haze, due to the tiny water droplets or solid particles suspended in the air, brings many inconveniences to daily life
This paper proposes a Polarization-based Self-supervised Dehazing Network named PSDNet that combines the difference of polarization information with deep learning to eliminate the influence of haze on the image
Haze-free images are not required as Ground Truth (GT) for constraint during all training processes, which reduces the dependence on data
Summary
The existence of haze, due to the tiny water droplets or solid particles suspended in the air, brings many inconveniences to daily life. The air can no longer be regarded as an isotropic medium which leads to scattering of the transmitted light. The scene image received by the camera or human eyes has a severe degradation. As the distance from the target increases or the concentration of suspended particles increases, the scattering becomes more and more serious. The details of the distant target are more severely lost, and the contrast of the captured image is reduced more. Eliminating the influence of haze on the collected image is often required which can make it easier for the observer to identify the target
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