Polarization-Based Haze Removal Using Self-Supervised Network

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.

Data-driven polarimetric imaging: a review

“…Intensity information is the most common complement based on the OPI and PPFM 53,[55][56][57]66,70,74,196 . Similarly, the spectrum 86 and phase 71,75 are general additions to the polarization information. In 3D shape reconstruction tasks, there are different complements such as zenith and azimuth angle maps derived from specular and diffuse reflection 64,65 , viewing encoding, encoded AoP 67 , normalized color 80 , and physics-based prior confidence 81 based on different conditions.…”

“…Furthermore, physical modes are crucial during network training, such as polarimetric descattering models, three-dimensional imaging models, Fresnel equations, Mueller matrix interpretation models, and other traditional polarization models. The preliminary methods are often end-to-end architectures 43,46,47,56,64,66−70 , suggesting that the polarization information is input into the network to generate the desired outputs directly; however, the physical models are gradually guided 46,47,49,71 or integrated into network training 48,72−74 . Furthermore, the physical model and its inverse process can form a self-supervised closed loop to achieve better performance.…”

“…This process contributes to future research and development. Based on the polarimetric information fused into the network, other physical properties of light have also been introduced in network training, such as spectrum 172 and phase 71,75 . Finally, the application fields tend to be semantic tasks based on convolutional enhancement or restoration imaging.…”

“…Finally, the application fields tend to be semantic tasks based on convolutional enhancement or restoration imaging. Initially, image processing was the main field applied by data-driven polarimetric imaging, such as descattering imaging [46][47][48][49]66,70,72,76 , denoising 45,77,78 , demosaicing 43,44,68 , dynamic range enhancement 79 , reflection removal 53,73,74 , low-light imaging 42,50 , and even 3D reconstruction shape 64,65,67,71,80,81 . Next, semantic tasks appeared gradually similar to semantic segmentation 56,57,69,82,83 , camouflage object detection 84 , classification 60,61 , pathological diagnosis 62,63,85,86,87 .…”

“…Based on a polarimetric Bidirectional Reflectance Distribution Function (pBRDF) model and real polarization scene rendering, Kondo et al applied rendered polarized images to train a network for an accurate surface normal estimation 71 . A physics-based renderer was built to simulate the polarization behavior of the rays based on the proposed pBRDF model for each material.…”