Objective

2

Image dehazing is a process of reducing the degradation effect of low-visibility imaging environment

such as fog

sputum

and sand

and improving the quality of image information acquisition. Image dehazing mainly solves the problems of image feature information blur

low contrast

gray-level concentration

and color distortion. At present

image dehazing methods are mainly divided into two categories

namely

image restoration and image enhancement. A dehazing algorithm based on image restoration is used to establish a physical model of image degradation in restoring the clear image in a targeted manner by analyzing the degradation mechanism of the image and by using prior knowledge or assumptions. The dehazing algorithm is more targeted compared with image enhancement algorithms. Deblurring is better and image information is complete

which should be investigated. Therefore

an image dehazing algorithm based on mixed prior and weighted guided filter (MPWGF) is proposed to eliminate the prior blind zone and improve the sharpness of the edge detail of haze-free images.

Method

2

First

a new method of atmospheric light value estimation is proposed to reduce the limitation of atmospheric light value estimation and utilize the advantage of mixed prior conditions. Pixel positions of 0.1% before brightness in dark channel and depth maps are extracted

and coordinate points extracted from the two images are compared. The coordinate points are retained when two images are observed simultaneously; otherwise

the values with the highest brightness that correspond to the remaining coordinate points in the original image are eliminated. This method can eliminate outliers to some extent and improves the accuracy of atmospheric light estimation. Then

mixed prior theory is used to calculate the atmospheric transmission of the double-constraint region

which eliminates the prior blind zone to a certain extent. This theory improves the robustness of the dehazing algorithm. The dark channel prior (DCP) and color attenuation prior (CAP) have good recovery effects and can compensate for the existence of the prior blind zone. Therefore

an effective region segmentation method is proposed to segment the bright and foggy regions of blurred images. On the basis of regional characteristics

DCP and CAP are used to obtain atmospheric transmittance maps to solve the prior blind region problem in which a single prior estimation method affects the robustness of the restoration algorithm. Finally

an adaptive guided filter algorithm is used to optimize the transmission map

which improves the sharpness of the image edge details. The obtained coarse transmittance map should be refined to eliminate the halo and block artifacts that locally exist in the restored image. A traditional transmittance map optimization algorithm has poor edge retention capability and serious loss of details. Thus

this study proposes an adaptive weighted guidance filtering algorithm based on the traditional algorithm. The edge detail improvement of the fine transmittance map is achieved by adding an adaptive weighted factor.

Result

2

In this study

the general dehazing test image and foggy image captured by a small UAV are taken as experimental objects. The rationality of the improved methods and the superiority of the overall algorithm are verified by comparing and analyzing the restoration effects of the four combined step algorithms. Experimental results show that the mixed prior theory improves the distortion of the dark priori in the bright region and the deficiency of CAP in dense fog processing and achieves better visual effect. Weighted guided filtering improves the image edge blurring and makes the image edge details clear after restoration. In comparison with other comparison algorithms

the proposed method has better visual effects

and the edge details of haze-free images are more evident. The average increase of comprehensive evaluation index is large.

Conclusion

2

For the restoration of hazy images

the superiority of the proposed improved model is demonstrated through theoretical analysis and experimental verification. The restoration of the proposed algorithm is better than that with the traditional algorithm. The main conclusions are summarized as follows. Mixed prior theory can improve the prior blind area problem in DCP and CAP theories to a certain extent

and the effect of image defogging is better. The adaptive guided filtering algorithm can optimize the transmittance image better and improve the edge sharpness of the defogging image. The image defogging effect can be improved by combining hybrid prior theory with the adaptive guided filtering algorithm. Under the same conditions

the proposed algorithm has better image restoration effect compared with the traditional fog removal algorithm. In this study

several limitations are observed in the parameter setting of regional segmentation

such as the adjustment of parameters through experience and insufficient certain theoretical basis. The parameter setting will be investigated in our future study. MPWGF has broad application prospects in image restoration

artificial intelligence

photogrammetry

and other fields.