Polarimetric Dehazing Method Based on Image Fusion and Adaptive Adjustment Algorithm

Abstract

To improve the robustness of current polarimetric dehazing scheme in the condition of low degree of polarization, we report a polarimetric dehazing method based on the image fusion technique and adaptive adjustment algorithm which can operate well in many different conditions. A splitting focus plane linear polarization camera was employed to grab the images of four different polarization directions, and the haze was separated from the hazy images by low-pass filtering roughly. Then the image fusion technique was used to optimize the method of estimating the transmittance map. To improve the quality of the dehazed images, an adaptive adjustment algorithm was introduced to adjust the illumination distribution of the dehazed images. The outdoor experiments have been implemented and the results indicated that the presented method could restore the target information obviously, and both the visual effect and quantitative evaluation have been enhanced.

Principle and Implementation of Stokes Vector Polarization Imaging Technology

“…The polarization imaging of split focal plane based on Stokes vector proposed a new method to improve the quality of defogging images. Yu Lei et al proposed a polarization defogging method based on image fusion and adaptive adjustment algorithm in 2021 [51]. A split-focal plane linear polarization camera (FLIR, BFS-U3-512P-C) was used to collect images with four different polarization directions, and then low-pass filtering was performed on the images to perform rough haze separation to reduce the adverse effects of noise.…”

“…This method cannot operate effectively under the condition of low DOP of background scattered light, reduce the adverse effects of haze, and ultimately enhance the visual effect and quantitative evaluation. The physical model of atmospheric scattering and Polarimetric imaging proposed in [51], as shown in Figure 10.…”

“…This method cannot operate effectively under the condition of low DOP of background scattered light, reduce the adverse effects of haze, and ultimately enhance the visual effect and quantitative evaluation. The physical model of atmospheric scattering and Polarimetric imaging proposed in [51], as shown in Figure 10. Reference [8] proposed a method to suppress scattering by Stokes vector polarization, using the total radiation polarization information of the gradient image pixel level prior knowledge to build a global model, the method can solve the target image of a light polarization degree of global change, achieve the goal calculated polarization of the light with each pixel in the scene, to effectively restore the details of the underwater scene.…”

SPIDE-Net: Spectral Prior-Based Image Dehazing and Enhancement Network

“…Second approach is to classify the algorithms based on image enhancement [8], [9], image fusion [10], [11] and Deep-Learning based methods [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38]. Moreover, we can also classify dehazing algorithms depending on the input images as single or multiple [12]. However, single image based strategy has mostly been used in last decades.…”