Synthetic Aperture Radar Image Denoising Algorithm Based on Deep Learning

Fig. 7. Denoising effect comparison of airport image. (a) Original image; (b) noisy image; (c) denoised image obtained by WNNM; (d) denoised image obtained by SAR-BM3D; (e) denoised image obtained by SAR-CNN; (f) denoised image obtained by MALNet

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Fig. 8. Denoising effect comparison of coast image. (a) Original image; (b) noisy image; (c) denoised image obtained by WNNM; (d) denoised image obtained by SAR-BM3D; (e) denoised image obtained by SAR-CNN; (f) denoised image obtained by MALNet

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Fig. 9. Denoising effect comparison of mountain image. (a) Original image; (b) noisy image; (c) denoised image obtained by WNNM; (d) denoised image obtained by SAR-BM3D; (e) denoised image obtained by SAR-CNN; (f) denoised image obtained by MALNet

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Table 1. Structural parameters of MALNet

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Table 1. Structural parameters of MALNet

Local	Kernel size	Stride	Padding	Channel	Pooling
$[F (0) ‑ F {(1)]}_{1}$	5×5	1	2	64
$[F (0) ‑ F {(1)]}_{2}$	3×1	1	（1，0）	64
$[F (0) ‑ F {(1)]}_{3}$	1×3	1	（0，1）	64
$[F (0) ‑ F {(1)]}_{4}$	3×3	1	1	64
F（1）	3×3	1	1	32
F（2）				32
$X_{0}$	3×3	1	1	32
$X_{1}$ ‑ $X_{2}$	3×3	1	1	32
$X_{2}$ ‑ $X_{3}$	3×3	1	1	32
$X_{3}$ ‑ $X_{4}$	3×3	1	1	32	AvgPool（2×2）
$X_{0}$ ‑ $X_{4}$	3×3	1	1	32	MaxPool（3×3）
F（3）				64
F（4）	3×3	1	1	1

Table 2. Parameters of experimental platform

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Table 2. Parameters of experimental platform

Hardware	Description
CPU	12th Gen intel（R）Core（TM）i9-12900KF @ 3.19 GHz
GPU	NVIDIA GeForce RTX 3090
Memory	32 GB
System	Windows 10
Video memory	24 GB
Language framework	Anconda + Python3.8.13 + Pytorch1.11.0

Table 3. Denoising level (PSNR) of each algorithm for each type of SAR image under different noise levels

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Table 3. Denoising level (PSNR) of each algorithm for each type of SAR image under different noise levels

Image	$σ$	WNNM	SAR-BM3D	SAR-CNN	MALNet
Airport	20	28.87	29.28	31.85	33.42
	30	26.62	27.13	30.14	31.11
	40	26.35	26.52	29.86	29.97
	45	25.28	25.93	27.77	29.31
	50	25.17	24.01	27.67	27.82
Mountain	20	28.15	29.13	31.10	33.76
	30	26.53	27.65	30.03	31.17
	40	25.72	26.04	28.53	29.59
	45	25.14	25.99	27.17	27.85
	50	24.05	25.92	27.31	26.62
Coast	20	28.22	30.21	31.01	33.83
	30	26.63	28.52	29.43	31.58
	40	26.55	27.73	28.59	29.98
	45	25.39	26.24	26.87	28.93
	50	24.37	25.55	26.12	26.11

Table 4. Denoising level (SSIM) of each algorithm for each type of SAR image under different noise levels

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Table 4. Denoising level (SSIM) of each algorithm for each type of SAR image under different noise levels

Image	$σ$	WNNM	SAR-BM3D	SAR-CNN	MALNet
Airport	20	0.7929	0.8324	0.8923	0.9257
	30	0.7538	0.7826	0.8621	0.8969
	40	0.7063	0.7505	0.8669	0.8671
	45	0.6818	0.6867	0.7926	0.8309
	50	0.6821	0.6027	0.7818	0.7892
Mountain	20	0.7867	0.8354	0.9012	0.9354
	30	0.7128	0.7891	0.9010	0.9194
	40	0.6957	0.7497	0.8725	0.8963
	45	0.6708	0.7134	0.7971	0.8361
	50	0.6684	0.6808	0.8211	0.7655
Coast	20	0.7911	0.8561	0.9036	0.9253
	30	0.7187	0.7899	0.8947	0.8987
	40	0.7064	0.7587	0.8809	0.8827
	45	0.6817	0.7308	0.7437	0.8817
	50	0.6512	0.6915	0.7018	0.7939

Table 5. Image entropy of each algorithm for each type of SAR image under different noise levels unit: $b i t \cdot p i x e l^{- 1}$

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Table 5. Image entropy of each algorithm for each type of SAR image under different noise levels unit: $b i t \cdot p i x e l^{- 1}$

Image	$σ$	WNNM	SAR-BM3D	SAR-CNN	MALNet
Airport	20	6.5312	6.3112	6.8529	6.1879
	30	6.8254	6.7709	7.2964	6.3542
	40	7.1596	6.9606	7.5976	6.8793
	45	7.3177	7.2318	7.2151	6.9074
	50	7.8325	7.6321	7.4647	7.5258
Mountain	20	6.4219	6.3864	7.0158	6.0872
	30	6.8297	6.5913	7.2479	6.3173
	40	7.3541	7.1291	7.8931	6.7966
	45	7.5627	7.3945	7.9315	6.9037
	50	7.9861	7.4837	8.0147	7.5813
Coastal	20	6.5207	6.2186	6.9615	6.2157
	30	6.7896	6.4517	7.3541	6.7959
	40	7.1584	6.9526	7.7987	6.8394
	45	7.6983	7.0118	7.8424	7.0288
	50	7.9527	7.3189	7.8951	7.8198

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Xiangwei Fu, Huilin Shan, Lü Zongkui, Xingtao Wang. Synthetic Aperture Radar Image Denoising Algorithm Based on Deep Learning[J]. Acta Optica Sinica, 2023, 43(6): 0610002

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Paper Information

Category: Image Processing

Received: Jul. 7, 2022

Accepted: Aug. 25, 2022

Published Online: Mar. 13, 2023

The Author Email: Shan Huilin (shanhuilin@nuist.edu.cn)

DOI:10.3788/AOS221437

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Structural parameters of MALNet

Table 1. Structural parameters of MALNet

Table 2. Parameters of experimental platform

Table 2. Parameters of experimental platform

Table 3. Denoising level (PSNR) of each algorithm for each type of SAR image under different noise levels

Table 3. Denoising level (PSNR) of each algorithm for each type of SAR image under different noise levels

Table 4. Denoising level (SSIM) of each algorithm for each type of SAR image under different noise levels

Table 4. Denoising level (SSIM) of each algorithm for each type of SAR image under different noise levels

Table 5. Image entropy of each algorithm for each type of SAR image under different noise levels unit: bit⋅pixel-1

Table 5. Image entropy of each algorithm for each type of SAR image under different noise levels unit: bit⋅pixel-1

Table 5. Image entropy of each algorithm for each type of SAR image under different noise levels unit: $b i t \cdot p i x e l^{- 1}$

Table 5. Image entropy of each algorithm for each type of SAR image under different noise levels unit: $b i t \cdot p i x e l^{- 1}$