# Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition

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## Abstract

**:**

## 1. Introduction

## 2. Related Works

#### 2.1. Statistical Model of SAR Speckle

#### 2.2. Frequency Domain Decomposition Model

## 3. Methodology

#### 3.1. General Network Architecture

#### 3.2. Network Subblock Structure

#### 3.3. Overall Network Training

## 4. Results and Discussion

#### 4.1. Experimental Setting

#### 4.2. Comparative Methods and Quantitative Evaluations

#### 4.3. Simulated Data Experiments

#### 4.4. Real SAR Data Experiments

#### 4.5. Ablation Study

#### 4.6. About Runtime and Number of Parameters

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 6.**Original images used in the test experiments with specific areas highlighted by red boxes for zoomed views.

**Figure 26.**Average PSNR and SSIM of model with CA and without CA in the test datasets. (

**a**) PSNR. (

**b**) SSIM.

**Figure 27.**Average PSNR and SSIM of model with different blocks in the test datasets. (

**a**) PSNR. (

**b**) SSIM.

Methods | L = 1 (PSNR/SSIM) | L = 2 (PSNR/SSIM) | L = 4 (PSNR/SSIM) |
---|---|---|---|

PPB | 23.19/0.5790 | 24.81/0.6637 | 26.36/0.7366 |

SAR-BM3D | 24.65/0.6733 | 26.28/0.7407 | 27.90/0.7990 |

SAR-POTDF | 23.32/0.6120 | 25.58/0.7086 | 27.61/0.7826 |

FANS | 24.43/0.6601 | 26.22/0.7313 | 27.95/0.7909 |

ID-CNN | 25.21/0.6871 | 26.92/0.7535 | 28.56/0.8073 |

SAR-DRN | 25.42/0.7035 | 27.01/0.7623 | 28.61/0.8124 |

HDRANet | 25.41/0.7010 | 26.83/0.7528 | 28.55/0.8099 |

SAR-RDCP | 25.53/0.7095 | 27.19/0.7690 | 28.72/0.8166 |

SAR-CAM | 25.61/0.7142 | 27.19/0.7693 | 28.71/0.8161 |

SAR−FDD | 26.02/0.7348 | 27.59/0.7864 | 29.15/0.8329 |

SAR−FDD−B | 25.94/0.7263 | 27.48/0.7797 | 28.96/0.8251 |

Sensor | Method | ENL | MoI | MoR | EPD-ROA | ||
---|---|---|---|---|---|---|---|

Region I | Region II | HD | VD | ||||

Noerdlingen | PPB | 137.91 | 340.42 | 0.9624 | 0.9626 | 0.9432 | 0.9511 |

SAR-BM3D | 111.11 | 51.51 | 0.9656 | 0.9654 | 0.9366 | 0.9515 | |

SAR-POTDF | 167.94 | 367.51 | 0.9632 | 0.9665 | 0.9319 | 0.9316 | |

FANS | 152.18 | 481.57 | 0.9598 | 0.9634 | 0.9211 | 0.9380 | |

ID-CNN | 163.55 | 179.27 | 0.9615 | 0.9626 | 0.8898 | 0.8923 | |

SAR-DRN | 169.27 | 188.83 | 0.9635 | 0.9643 | 0.9288 | 0.9308 | |

HDRANet | 179.32 | 222.61 | 0.9654 | 0.9661 | 0.9282 | 0.9312 | |

SAR-RDCP | 171.92 | 259.43 | 0.9649 | 0.9656 | 0.9266 | 0.9347 | |

SAR-CAM | 179.52 | 557.32 | 0.9682 | 0.9699 | 0.8883 | 0.9063 | |

SAR−FDD | 174.40 | 177.04 | 0.9675 | 0.9663 | 0.9411 | 0.9395 | |

SAR−FDD−B | 172.01 | 609.34 | 0.9643 | 0.9660 | 0.9040 | 0.9138 | |

Horse track | PPB | 138.42 | 117.20 | 0.9523 | 0.9478 | 0.9469 | 0.9457 |

SAR-BM3D | 86.64 | 39.31 | 0.9587 | 0.9529 | 0.9462 | 0.9731 | |

SAR-POTDF | 112.57 | 84.70 | 0.9712 | 0.9680 | 0.9558 | 0.9555 | |

FANS | 135.09 | 130.45 | 0.9532 | 0.9537 | 0.9268 | 0.9518 | |

ID-CNN | 135.24 | 99.11 | 0.9587 | 0.9540 | 0.9008 | 0.9073 | |

SAR-DRN | 94.83 | 84.75 | 0.9587 | 0.9540 | 0.9479 | 0.9473 | |

HDRANet | 107.91 | 70.47 | 0.9604 | 0.9594 | 0.9386 | 0.9457 | |

SAR-RDCP | 118.20 | 90.98 | 0.9598 | 0.9537 | 0.9415 | 0.9525 | |

SAR-CAM | 127.56 | 117.71 | 0.9611 | 0.9565 | 0.9315 | 0.9581 | |

SAR−FDD | 93.42 | 73.23 | 0.9651 | 0.9567 | 0.9685 | 0.9726 | |

SAR−FDD−B | 342.87 | 494.92 | 0.9588 | 0.9580 | 0.9494 | 0.9700 | |

Volgograd | PPB | 93.28 | 146.67 | 0.9668 | 0.9548 | 0.9256 | 0.9327 |

SAR-BM3D | 94.75 | 115.61 | 0.9747 | 0.9631 | 0.9037 | 0.9269 | |

SAR-POTDF | 179.10 | 102.52 | 0.9911 | 0.9762 | 0.9163 | 0.9276 | |

FANS | 125.86 | 121.59 | 0.9779 | 0.9725 | 0.8998 | 0.9210 | |

ID-CNN | 360.75 | 178.11 | 0.9749 | 0.9603 | 0.8782 | 0.8939 | |

SAR-DRN | 470.37 | 164.41 | 0.9723 | 0.9577 | 0.9019 | 0.9153 | |

HDRANet | 509.75 | 255.59 | 0.9752 | 0.9621 | 0.9059 | 0.9257 | |

SAR-RDCP | 424.98 | 178.73 | 0.9711 | 0.9556 | 0.9030 | 0.9210 | |

SAR-CAM | 754.82 | 284.08 | 0.9746 | 0.9620 | 0.8706 | 0.8953 | |

SAR−FDD | 249.87 | 106.28 | 0.9624 | 0.9533 | 0.9283 | 0.9443 | |

SAR−FDD−B | 954.39 | 849.73 | 0.9656 | 0.9632 | 0.8880 | 0.9082 |

Loss Function | PSNR (dB) | SSIM |
---|---|---|

MSE | 29.1388 | 0.8315 |

DG | 29.1418 | 0.8318 |

$\mathrm{DG}+{L}_{\mathrm{TV}}$ | 29.1357 | 0.8317 |

$\mathrm{DG}+{L}_{\mathrm{T}{\mathrm{V}}^{2}}$ | 29.1423 | 0.8316 |

$\mathrm{DG}+{L}_{\mathrm{TV}}$$+{L}_{\mathrm{T}{\mathrm{V}}^{2}}$ | 29.1501 | 0.8329 |

Method | ID-CNN | SAR-DRN | HDRANet | SAR-RDCP | SAR-CAM | SAR−FDD | SAR- FDDL-B |
---|---|---|---|---|---|---|---|

Parameters | 223,104 | 185,857 | 112,611 | 272,196 | 3,317,284 | 377,537 | 377,537 |

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**MDPI and ACS Style**

Zhao, X.; Ren, F.; Sun, H.; Qi, Q.
Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition. *Electronics* **2024**, *13*, 490.
https://doi.org/10.3390/electronics13030490

**AMA Style**

Zhao X, Ren F, Sun H, Qi Q.
Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition. *Electronics*. 2024; 13(3):490.
https://doi.org/10.3390/electronics13030490

**Chicago/Turabian Style**

Zhao, Xueqing, Fuquan Ren, Haibo Sun, and Qinghong Qi.
2024. "Synthetic Aperture Radar Image Despeckling Based on a Deep Learning Network Employing Frequency Domain Decomposition" *Electronics* 13, no. 3: 490.
https://doi.org/10.3390/electronics13030490