Research Of Improved Single Image Haze Removal Algorithm Based On Physical Model

In recent years, with the frequent occurrence of haze weather, the tiny particles such as water droplets and dust in the air seriously affect the scattering and refraction of light, this lead to the low visibility of outdoor scenes. As a result, the image obtained by the visual imaging system has the obvious degradation of color distortion, scene information loss and contrast reduction. This greatly affects and limits the effectiveness of the visual system, thus affecting the visual effect of the image and image feature extraction. Therefore, the restoration of single degraded image in fog and haze conditions has important theoretical significance and application value.In this thesis, we mainly study the dark channel prior theory, the guided filtering algorithm and the algorithm based on the atmospheric dissipation function. And put forward two improved optimization algorithm based on these algorithms.Algorithm one: Single image in-depth dehazing algorithm based on optimization of the guide imageFirstly, this algorithm makes in-depth analysis of the application of the guide filtering and dark channel prior to the defogging and find defects. Based on bilateral filtering and guided filtering algorithms, the local mean and standard deviation are obtained by the bilateral filtering and median filtering algorithm process the minimum image respectively. A new guide map is obtained by the average approximation minimum value minus the three times of variance. Then, a kind of dark region is defined according to the intensity of the guide image,obtaining the optimal guide image by comparing with the minimum image. The atmospheric veil map is obtained by using the fast guided filter, and then the clear image is restored according to the atmospheric scattering physical model. Compared with the traditional guided filtering algorithm, this method can effectively remove the Halo effect. At the same time,because of the in-depth process of the guide map, the guide map is more smooth, so the close range and the prospect of fog effect is ideal, to meet the requirements of the depth of fog. In addition, the overall details of the restored image is rich, the overall color is true and natural.However, when the fog concentration is high or in the night scene, the effect of this algorithm is not obvious, there are some limitations in practical application.Algorithm two: Fast light compensation defogging algorithm based on semi-inverse image guided filteringThis algorithm will take the threshold value adjusted semi-inverse image as the guide image. Based on pixel level, comparing the hue value for each pixel, no filtering process is used. The computation time and space complexity are greatly reduced. Moreover, the solution time of the process is small and the efficiency is further improved. The total time of the algorithm is greatly reduced, so that the algorithm can achieve real-time defogging. The average value of the first 1% brightest pixels of the original fog image is used as the reference white light compensation to further adjust the brightness of the restored image. The restored image is clear and natural, and the contrast has been greatly improved, but the effect is not obvious for the image of the dense fog or the image taken in the night scene.