## Weimin Zhou## |

Index | Operating System | CPU | PyTorch version | Python version | MATLAB version | RAM |
---|---|---|---|---|---|---|

Parameter | Linux Mint 20 | 2.40 GHz | 1.2.0 | 3.6.10 | MATLAB r2020a | 8 GB |

Table 2.

Category | Name | Fog image volume | Fog free image volume |
---|---|---|---|

Training set | ITS | 8,563 | 2,610 |

I-HAZE | 470 | 130 | |

Test set | O-HAZE | 1,249 | 307 |

SOTS | 109 | 23 | |

HSTS | 567 | 89 | |

RTTS | 95 | 10 |

Under the setting of the above experimental environment and dataset, the experimental research was carried out. The improved dark channel image dehazing method [2], the polarization image dehazing enhancement algorithm [3], the image enhancement dehazing algorithm [4] and the multi-level feature gradual thinning and edge enhancement dehazing algorithm [5] were compared with the proposed algorithm. The comparison results are analyzed as follows.

A foggy image was arbitrarily selected in the dataset, and the dehazing enhancement algorithm for polarized images based on the dark channel prior principle, the image enhancement dehazing algorithm based on weighted and adaptive guidance coefficients and the proposed algorithm were used for dehazing enhancement processing. The results are shown in Fig. 3.

According to Fig. 3, the visual effect of the proposed algorithm was better, which could not only effectively achieve the effect of dehazing, but also highlighted the details of the scene in bright places, and maintained the details of the scene in the deeper colors. The visual effect of the image was obviously better than that obtained by the traditional algorithm, the color fidelity was high, and the structural information was clear.

To further measure the image quality, this study used information entropy, peak signal-to-noise ratio (PSNR), structural similarity and other evaluation indicators. The calculation formula is as follows.

In the formula (12), [TeX:] $$P\left(\rho_i\right)$$ is the probability of [TeX:] $$\rho_i ; L_i$$ means the number of gray levels of the image. The larger the entropy value of the image, the greater the amount of information, and the richer the detailed information of the image.

The information entropy after image processing by applying five methods is shown in Fig. 4.

PSNR is the most widely used evaluation index in the field of image processing. The greater its value, the smaller the degree of deterioration of the processed image and the less distortion compared with the original image. The PSNR is calculated as follows:

In the formula (13), [TeX:] $$\varsigma$$ expresses the number of sampling bits, which is usually set to 8.

The PSNR after applying five methods is shown in Fig. 5.

Structural similarity is used to evaluate the ability of an algorithm to preserve structural information, and the higher the value, the better. Given two images [TeX:] $$I_1 \text { and } I_2 \text {, }$$ the structural similarity calculation formula is as follows:

In the formula (14), [TeX:] $$\varphi_{I_1} \text { and } \varphi_{I_2}$$ represent the average of [TeX:] $${I_1} \text { and } {I_2}$$ respectively; [TeX:] $$\pi_{I_1}^2 \text { and } \pi_{I_2}^2$$ stand for the variance of [TeX:] $${I_1} \text { and } {I_2}$$ respectively; [TeX:] $$\pi_{I_1 I_2}$$ indicate the covariance of [TeX:] $${I_1} \text { and } {I_2}$$; [TeX:] $$\tau_1 \text { and } \tau_2$$ both indicate constants.

The structural similarity after applying five methods is shown in Fig. 6.

Combined with the above objective evaluation results, the information entropy of the paper design algorithm was higher than 0.7bit, the peak SNR was higher than 40dB, and the structure similarity wad higher than 80%. The above indexes were higher than the other four traditional algorithms, which could effectively improve the evaluation index, obtain better image color, and improve the effect of the image recovery.

To improve the image restoration effect and solve the unclear restoration of details, low degree of tone restoration and loss of image details, an image dehazing enhancement algorithm based on mean-guided filtering was proposed. The main research results are as follows:

(1) The proposed algorithm had better visual effect, high color fidelity and clear structural information, and its image visual effect was obviously better than that obtained by traditional algorithms. (2) The information entropy, PSNR and structure similarity of the proposed algorithm were higher than those of the traditional algorithm. In this algorithm, the information entropy was higher than 0.7bit, the peak SNR was higher than 40dB, and the structural similarity was higher than 80%. Therefore, better image dehazing effect could be obtained and the image quality could be guaranteed. (3) Through the implementation of image dehazing, compared with the traditional dehazing algorithm, the proposed algorithm added a median filter to deal with image details. At the same time, to ensure the image quality, it maintained the jump of abrupt regions during noise processing. The final experimental results also showed that the proposed algorithm was superior to other algorithms in image detail processing, demisting effect, etc. However, the algorithms studied also have shortcomings. In image dehazing, the processing of image denoising due to environmental changes has not been fully considered. Later, more complex environmental issues need to be considered to improve the overall performance of the algorithm.

He born in Taizhou in 1980, is a doctoral student in control science and engineering of Zhejiang University of Technology and a computer teacher of Taizhou Vocational and Technical College, Zhejiang, China. He received the bachelor’s degrees in information and computing sciences from the Chongqing Three Gorges University in 2003, and the master’s degrees in electronics and communication engineering from the Anhui Uni-versity in 2011. His main research interests are computer vision and image processing.

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