# Defogging Algorithm Based on Polarization Characteristics and Atmospheric Transmission Model

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Atmospheric Transmission Model

## 3. Stokes Vector and Polarized Images

#### 3.1. Extraction of Scene Polarization Characteristics

#### 3.2. Joint Segmentation of Sky Region

_{a}and A

_{inf}caused by interferences in the sky region is prevented using statistics.

#### 3.3. Calculation of Polarized-Difference Image

## 4. Calculation of Air-Light Intensity Image Based on Gaussian Blur Filtering

#### Calculation of Light Intensities A and D

## 5. Estimation of Transmission Map t and Restoration of a Fog-Free Image

## 6. Experimental Analysis

## 7. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Segmentation example of sky region, (

**a**–

**h**) show the different results of the sky segmentation under different parameters, (

**a**) th1 = 60, th2 = 60, th3 = 180, (

**b**) th1 = 70, th2 = 70, th3 = 190, (

**c**) th1 = 70, th2 = 80, th3 = 190, (

**d**) th1 = 70, th2 = 80, th3 = 200, (

**e**) th1 = 70, th2 = 90, th3 = 200, (

**f**) th1 = 70, th2 = 95, th3 = 200, (

**g**) th1 = 70, th2 = 95, th3 = 205, (

**h**) th1 = 70, th2 = 95, th3 = 215.

**Figure 3.**Result of the Gaussian blur filtering, (

**a**–

**d**) the polarized air-light images at fours angle, (

**e**–

**h**) the polarized object images at fours angles, (

**i**) the light intensity of the air-light, (

**j**) the light intensity of the object, (

**k**) DOP of the object.

**Figure 5.**The comparison between original hazy image and dehazed image, (

**a**) original hazy image, (

**b**) dehazed image.

**Figure 7.**The comparison between different methods, (

**a**) the original hazy image, (

**b**) the result of dark channel, (

**c**) the result of SIDMP, (

**d**) the result of proposed method.

**Figure 8.**The comparison of the enlarged details, (

**a**) the original image, (

**b**) the result of dark channel, (

**c**) the result of SIDMP, (

**d**) the result of proposed.

**Figure 9.**Histogram comparison results, (

**a**) result of the original hazy images, (

**b**) result of dark channel, (

**c**) result of SIDMP, (

**d**) result of proposed method.

**Figure 10.**The comparison of deferent methods, (

**a**) the original hazy image, (

**b**) the result of DTMS, (

**c**) the result of proposed method.

**Figure 11.**Histogram comparison results, (

**a**) the result of original image, (

**b**) the result of DTMS, (

**c**) the result of proposed.

**Table 1.**Quantitative evaluation of defogging algorithms in the three scenes shown in Figure 7.

Grayscale Standard Deviation | Scenario1 | Scenario2 | Scenario3 |
---|---|---|---|

Original | 37.7078 | 56.9911 | 59.3694 |

Dark channel prior | 68.4882 | 86.6204 | 84.1204 |

SIDMP | 70.3881 | 83.5740 | 91.6790 |

Proposed | 64.7314 | 77.6341 | 83.3641 |

Average Gradient | Scenario1 | Scenario2 | Scenario3 |

Original | 0.0036 | 0.0045 | 0.0080 |

Dark channel | 0.0050 | 0.0066 | 0.0084 |

SIDMP | 0.0058 | 0.0067 | 0.0115 |

Proposed | 0.0091 | 0.0096 | 0.0176 |

NIQE | Scenario4 | Scenario5 |
---|---|---|

Original | 4.8812 | 5.3302 |

DTMS | 5.1897 | 5.6805 |

Proposed | 4.3335 | 4.2907 |

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**MDPI and ACS Style**

Ling, F.; Zhang, Y.; Shi, Z.; Zhang, J.; Zhang, Y.; Zhang, Y.
Defogging Algorithm Based on Polarization Characteristics and Atmospheric Transmission Model. *Sensors* **2022**, *22*, 8132.
https://doi.org/10.3390/s22218132

**AMA Style**

Ling F, Zhang Y, Shi Z, Zhang J, Zhang Y, Zhang Y.
Defogging Algorithm Based on Polarization Characteristics and Atmospheric Transmission Model. *Sensors*. 2022; 22(21):8132.
https://doi.org/10.3390/s22218132

**Chicago/Turabian Style**

Ling, Feng, Yan Zhang, Zhiguang Shi, Jinghua Zhang, Yu Zhang, and Yi Zhang.
2022. "Defogging Algorithm Based on Polarization Characteristics and Atmospheric Transmission Model" *Sensors* 22, no. 21: 8132.
https://doi.org/10.3390/s22218132