# Convolutional Neural Network and Guided Filtering for SAR Image Denoising

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## Abstract

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## 1. Introduction

## 2. The Model of Image Denoising Based on the CNN Prior

#### 2.1. Image Denoising Model

#### 2.2. CNN Denoiser

## 3. Image Fusion-Based Guided Filtering

## 4. CNN Denoiser Prior and Guided Filtering for SAR Image Denoising

## 5. Experimental Results

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 6.**Denoised images using different level denoisers: (

**a**) the source image; (

**b**) the noise image; (

**c**) denoiser 5; (

**d**) denoiser 10; (

**e**) denoiser 15; (

**f**) denoiser 20; (

**g**) denoiser 25; (

**h**) the proposed algorithm.

**Figure 7.**Denoised images using different methods: (

**a**) Lee filter; (

**b**) BSS-SR; (

**c**) SAR-BM3D; (

**d**) CS-BSR; (

**e**) PPB; (

**f**) BWNNM; (

**g**) DnCNN; (

**h**) our method.

**Figure 9.**The denoised tree SAR images using all denoising methods: (

**a**) Lee filter; (

**b**) BSS-SR; (

**c**) SAR-BM3D; (

**d**) CS-BSR; (

**e**) PPB; (

**f**) BWNNM; (

**g**) DnCNN; (

**h**) our method.

**Figure 10.**The denoised city area SAR images using all denoising methods: (

**a**) Lee filter; (

**b**) BSS-SR; (

**c**) SAR-BM3D; (

**d**) CS-BSR; (

**e**) PPB; (

**f**) BWNNM; (

**g**) DnCNN; (

**h**) our method.

**Figure 11.**The denoised lake SAR image using all denoising methods: (

**a**) Lee filter; (

**b**) BSS-SR; (

**c**) SAR-BM3D; (

**d**) CS-BSR; (

**e**) PPB; (

**f**) BWNNM; (

**g**) DnCNN; (

**h**) our method.

**Figure 12.**The ratio images using all denoising methods for Figure 8. (

**a**) ratio image by using lee filter; (

**b**) ratio image by using BSS-SR; (

**c**) ratio image by using SAR-BM3D; (

**d**) ratio image by using CS-BSR; (

**e**) ratio image by using PPB; (

**f**) ratio image by using BWNNM; (

**g**) ratio image by using DnCNN; (

**h**) ratio image by using our method.

**Figure 13.**The ratio images using all denoising methods for Figure 9. (

**a**) ratio image by using lee filter; (

**b**) ratio image by using BSS-SR; (

**c**) ratio image by using SAR-BM3D; (

**d**) ratio image by using CS-BSR; (

**e**) ratio image by using PPB; (

**f**) Ratio image by using BWNNM; (

**g**) ratio image by using DnCNN; (

**h**) ratio image by using our method.

**Figure 14.**The ratio images using all denoising methods for Figure 10. (

**a**) ratio image using lee filter; (

**b**) ratio image using BSS-SR; (

**c**) ratio image using SAR-BM3D; (

**d**) ratio image using CS-BSR; (

**e**) ratio image using PPB; (

**f**) ratio image using BWNNM; (

**g**) ratio image using DnCNN; (

**h**) ratio image using our method.

Noise Variance | Denoising Methods | PSNR | ENL | EPI | SSIM |
---|---|---|---|---|---|

0.04 | Lee filter | 32.51 | 6.94 | 0.82 | 0.75 |

BSS-SR | 31.65 | 7.84 | 0.71 | 0.62 | |

SAR-BM3D | 33.61 | 8.08 | 0.62 | 0.69 | |

CS-BSR | 31.48 | 8.92 | 0.63 | 0.58 | |

PPB | 30.87 | 7.56 | 0.66 | 0.71 | |

BWNNM | 33.42 | 7.28 | 0.66 | 0.75 | |

DnCNN | 31.51 | 6.02 | 0.71 | 0.72 | |

Our method | 38.29 | 6.46 | 0.81 | 0.94 | |

0.05 | Lee filter | 30.76 | 6.64 | 0.66 | 0.70 |

BSS-SR | 31.58 | 7.85 | 0.75 | 0.62 | |

SAR-BM3D | 33.72 | 8.34 | 0.68 | 0.67 | |

CS-BSR | 31.49 | 8.96 | 0.79 | 0.58 | |

PPB | 33.42 | 7.56 | 0.73 | 0.71 | |

BWNNM | 32.93 | 7.35 | 0.77 | 0.74 | |

DnCNN | 32.98 | 6.95 | 0.74 | 0.77 | |

Our method | 39.06 | 6.57 | 0.80 | 0.94 | |

0.06 | Lee filter | 31.60 | 6.27 | 0.62 | 0.63 |

BSS-SR | 31.65 | 7.83 | 0.71 | 0.61 | |

SAR-BM3D | 34.28 | 8.63 | 0.65 | 0.66 | |

CS-BSR | 31.64 | 8.95 | 0.74 | 0.58 | |

PPB | 33.41 | 7.57 | 0.84 | 0.71 | |

BWNNM | 32.49 | 7.60 | 0.81 | 0.89 | |

DnCNN | 30.29 | 5.78 | 0.86 | 0.90 | |

Our method | 41.57 | 6.48 | 0.90 | 0.93 |

Denoising Methods | UM | ENL | EPI | SSIM |
---|---|---|---|---|

Lee filter | 27.30 | 3.17 | 0.72 | 0.93 |

BSS-SR | 33.01 | 4.46 | 0.93 | 0.94 |

SAR-BM3D | 32.10 | 3.82 | 0.84 | 0.96 |

CS-BSR | 34.98 | 5.37 | 0.95 | 0.97 |

PPB | 28.75 | 4.13 | 0.93 | 0.91 |

BWNNM | 32.51 | 4.18 | 0.95 | 0.94 |

DnCNN | 31.83 | 3.59 | 0.77 | 0.93 |

Our method | 25.40 | 3.61 | 0.98 | 0.98 |

Denoising Methods | UM | ENL | EPI | SSIM |
---|---|---|---|---|

Lee filter | 32.96 | 1.98 | 0.79 | 0.95 |

BSS-SR | 40.67 | 2.04 | 0.91 | 0.86 |

SAR-BM3D | 34.53 | 1.87 | 0.88 | 0.97 |

CS-BSR | 36.30 | 2.30 | 0.92 | 0.75 |

PPB | 31.02 | 1.93 | 0.83 | 0.94 |

BWNNM | 30.18 | 1.99 | 0.94 | 0.96 |

DnCNN | 32.54 | 1.80 | 0.80 | 0.93 |

Our method | 25.26 | 1.81 | 0.95 | 0.98 |

Denoising Methods | UM | ENL | EPI | SSIM |
---|---|---|---|---|

Lee filter | 39.60 | 3.72 | 0.74 | 0.93 |

BSS-SR | 38.51 | 3.78 | 0.90 | 0.84 |

SAR-BM3D | 31.96 | 3.35 | 0.89 | 0.97 |

CS-BSR | 31.03 | 4.32 | 0.92 | 0.73 |

PPB | 33.25 | 3.65 | 0.77 | 0.92 |

BWNNM | 31.45 | 3.73 | 0.95 | 0.93 |

DnCNN | 32.26 | 3.19 | 0.82 | 0.96 |

Our method | 30.60 | 3.26 | 0.96 | 0.98 |

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## Share and Cite

**MDPI and ACS Style**

Liu, S.; Liu, T.; Gao, L.; Li, H.; Hu, Q.; Zhao, J.; Wang, C.
Convolutional Neural Network and Guided Filtering for SAR Image Denoising. *Remote Sens.* **2019**, *11*, 702.
https://doi.org/10.3390/rs11060702

**AMA Style**

Liu S, Liu T, Gao L, Li H, Hu Q, Zhao J, Wang C.
Convolutional Neural Network and Guided Filtering for SAR Image Denoising. *Remote Sensing*. 2019; 11(6):702.
https://doi.org/10.3390/rs11060702

**Chicago/Turabian Style**

Liu, Shuaiqi, Tong Liu, Lele Gao, Hailiang Li, Qi Hu, Jie Zhao, and Chong Wang.
2019. "Convolutional Neural Network and Guided Filtering for SAR Image Denoising" *Remote Sensing* 11, no. 6: 702.
https://doi.org/10.3390/rs11060702