## Jun Cao## |

Project | He et al. [7] | Wang et al. [8] | Present study |
---|---|---|---|

Experimental platform | MATLAB R2016a | MATLAB R2016a | MATLAB R2016a |

Usage method | Image defogging method based on MALLAT algorithm | Single image defogging algorithm based on neural network ooptimization | Physical model and guided filtering of fog attenuation image |

Evaluating indicator | PSNR and SSIM | PSNR and SSIM | PSNR and SSIM |

Fog image format | PNG | PNG | PNG |

Image size with fog | 389×729 | 389×729 | 389×729 |

To evaluate the defogging effect of the proposed method on synthetic images, two image quality evaluation indices: peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) were used as indicators.

4.2.1 PSNR

Among them, PSNR realizes the evaluation of the overall similarity of the image, which is mainly based on the calculation of the error between the corresponding pixels between the defog image and the non-fog image, which focuses on the evaluation of the color deviation of the defog image and the distortion of the defog image. The larger the value of PSNR, the smaller the distortion of the defog image and the closer to the fog free image. The calculation method of PSNR is shown in formula (13):

In formula (13), MSE denotes the mean square error; [TeX:] $$M A X_I$$ denotes the maximum value in the image color value, which is usually 255.

4.2.2 Structural similarity

SSIM is an index reflecting the similarity between defog images and non-fog images. Three different standards of brightness, contrast and structure are used to evaluate the similarity of the two images. The larger the SSIM value, the more similar the defog image and non-fog image, and the better the defog effect. The calculation method of SSIM is shown in formula (14):

here [TeX:] $$\mu_x \text { and } \mu_y$$ denote the mean values of x and y, respectively; [TeX:] $$\sigma_x^2, \sigma_y^2$$ denotes the variance of x and y, respectively; [TeX:] $$\sigma_{xy}$$ denotes the covariance of x and y; [TeX:] $$C_1 \text{ and } C_2$$ denote two constants, which exist to prevent the denominator from being zero, resulting in system errors.

In order to obtain a better experimental defogging effect, the batch size in the experiment is set to 24. For the network optimization algorithm, the Adam algorithm is used to optimize the proposed method network, and the learning rate is set to 0.001. At the same time, the total number of training iterations of the proposed method is set to 3,000. The test data set consists of 700 images of different scenes taken on foggy days. The ratio between the test and training sets is 6:1. Once the entire dataset is trained, it is considered as one epoch, with a total of 50 epochs.

4.3.1 Image enhancement effect comparison

The image enhancement processing effects of the methods in references [7,8] and the method proposed in this study were comparatively analyzed.

The method in [7] has a significant enhancement effect on images with low illumination, which is not sufficient to restore the overall brightness. Local objects are too dark, but the enhanced image color is not natural enough. The method in [8] makes the color of the enhanced image white, which is not in accordance with the real visual effect; The enhancement effect of the proposed algorithm is natural in brightness recovery, and has a significant improvement in image detail texture recovery, which is in accordance with the normal visual effect. The contour and detail of the image retain more information (Fig. 3).

4.3.2 SSIM

To prove the image demisting effect of the proposed method in this study, three methods (He et al. [7], Wang et al. [8], and this study) are used to detect the SSIM value of the image after demisting.

It can be seen from Fig. 4 that the SSIM value of the method in this paper is the largest, with the SSIM value of 0.9616, which is 0.0944 higher than the SSIM value in [7], while the SSIM value in [8] is 0.8151. It can be seen that the image defogging effect of this method is better.

4.3.3 PSNR

To demonstrate the image defogging effect of the proposed method, three methods (He et al. [7], Wang et al. [8], and this study) are used to detect the image peak SNR after defogging.

According to the analysis of Table 2, when the number of training iterations is 500, the image PSNR of the method in [7] is 31.68 dB, the image PSNR of the method in [8] is 22.41 dB, and the image PSNR of the method in this paper is 32.63 dB. When the number of training iterations is 1,500, the image PSNR of the method in [7] is 15.74 dB, the image PSNR of the method in literature [8] is 12.76 dB, and the image PSNR of the proposed method is 16.68 dB. When the number of training iterations is 3000, the image PSNR of the method in [7] is 35.68 dB, the image PSNR of the method in [8] is 38.38 dB, and the image PSNR of the proposed method is 43.29 dB. The image PSNR of this method is much higher than that of other methods, which shows that the image defogging effect of this method is better.

This paper studies an image enhancement algorithm and image defogging method. The fog image is preprocessed by histogram equalization method; The physical model of atmospheric scattering is constructed, the image detail feature is enhanced by image enhancement method, and the visual effect of defogging image is enhanced by guided filtering method. The proposed method has a good defogging effect on the image.

When the number of training iterations is 3000, the image PSNR of this method is 43.29 dB. The image PSNR of the proposed method is much higher than that of other methods.

When the number of training iterations is 3,000, the similarity of image structure after defogging is 0.9616. After defogging, the image structure similarity of the proposed method is significantly higher than that of other methods, indicating better image-defogging effect.

She was born in Sichuan, China, in 1984. She received her bachelor’s degree in computer science and technology from Southwest University in 2006, and master’s degree in computer technology from Chongqing Normal University in 2017. Currently, she is a lecturer at Guang'an Vocational & Technical College. She has published a total of 6 papers. Her research interest covers image processing.

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