Remote-sensing images reconstruction based on adaptive dual-domain attention network

Scale	PSNR/SSIM
Scale	Bicubic	SRCNN	FSRCNN	VDSR	LGCNet	DCM	HSENet	TransENet	Ours
2	30.76/0.8789	32.84/0.9152	33.18/0.9196	33.38/0.9220	33.48/0.9235	33.65/0.9274	34.22/0.9327	35.43/0.9355	35.62/0.9717
3	27.46/0.7631	28.66/0.8038	29.09/0.8167	29.28/0.8232	29.28/0.8238	29.52/0.8349	30.00/0.8420	31.03/0.8526	31.10/0.8811
4	25.65/0.6725	26.78/0.7219	26.93/0.7267	26.85/0.7317	27.02/0.7333	27.22/0.7528	27.73/0.7623	28.74/0.7694	28.84/0.8003

Table 2. PSNR/SSIM results on the AID dataset (×2, ×3, and ×4)

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Table 2. PSNR/SSIM results on the AID dataset (×2, ×3, and ×4)

Scale	PSNR/SSIM
Scale	Bicubic	SRCNN	FSRCNN	VDSR	LGCNet	DCM	HSENet	TransENet	Ours
2	32.39/0.8906	34.49/0.9286	34.73/0.9331	35.05/0.9346	34.80/0.9320	35.21/0.9366	35.24/0.9368	35.28/0.9374	36.93/0.9617
3	29.08/0.7863	30.55/0.8372	30.98/0.8401	31.15/0.8522	30.73/0.8417	31.31/0.8561	31.39/0.8572	31.45/0.8595	32.96/0.8889
4	27.30/0.7036	28.40/0.7561	28.77/0.7729	28.99/0.7753	28.61/0.7626	29.17/0.7824	29.21/0.7850	29.38/0.7909	29.99/0.8177

Table 3. Average PSNR for each category with an upscaling factor of ×4 on the AID dataset

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Table 3. Average PSNR for each category with an upscaling factor of ×4 on the AID dataset

Class	PSNR/dB
Class	Bicubic	SRCNN	LGCNet	VDSR	DCM	HSENet	TransENet	Ours
Airport	27.03	28.17	28.39	28.82	28.99	29.03	29.23	29.31
Bareland	34.88	35.63	35.78	35.98	36.17	36.21	36.20	36.42
Baseball field	29.06	30.51	30.75	31.18	31.36	31.23	31.59	31.28
Beach	31.07	31.92	32.08	32.29	32.45	32.76	32.55	33.51
Bridge	28.98	30.41	30.67	31.19	31.39	31.30	31.63	30.83
Center	25.26	26.59	26.92	27.48	27.72	27.84	28.03	27.44
Church	22.15	23.41	23.68	24.12	24.29	24.39	24.51	24.62
Commercial	25.83	27.05	27.24	27.62	27.78	27.99	27.97	28.39
Dense residential	23.05	24.13	24.33	24.70	24.87	24.44	25.13	24.62
Desert	38.49	38.84	39.06	39.13	39.27	39.37	39.31	38.99
Farmland	32.30	33.48	33.77	34.20	34.42	33.90	34.58	34.19
Forest	27.39	28.15	28.20	28.36	28.47	38.31	28.56	28.37
Industrial	24.75	26.00	26.24	26.72	26.92	26.99	27.21	27.30
Meadow	32.06	32.57	32.65	32.77	32.88	32.74	32.94	33.30
Medium residential	26.09	27.37	27.63	28.06	28.25	28.11	28.45	26.94
Mountain	28.04	28.90	28.97	29.11	29.18	29.26	29.28	28.89
Park	26.23	27.25	27.37	27.69	27.82	28.23	28.01	28.11
Parking	22.33	24.01	24.40	25.21	25.74	26.17	26.40	26.01
Playground	27.27	28.72	29.04	29.62	29.92	31.18	30.30	32.00
Pond	28.94	29.85	30.00	30.26	30.39	30.40	30.53	30.33
Port	24.69	25.82	26.02	26.43	26.62	26.92	26.91	27.47
Railway station	26.31	27.55	27.76	28.19	28.38	28.47	28.61	28.42
Resort	25.98	27.12	27.32	27.71	27.88	27.99	28.08	27.66
River	29.61	30.48	30.60	30.82	30.91	30.88	31.00	30.28
School	24.91	26.13	26.34	26.78	26.94	27.51	27.22	27.52
Sparse residential	25.41	26.16	26.27	26.46	26.53	26.44	26.43	26.58
Square	26.75	28.13	28.39	28.91	29.13	29.05	29.39	28.79
Stadium	24.81	26.10	26.37	26.88	27.10	27.28	27.41	28.01
Storage tanks	24.18	25.27	25.48	25.86	26.00	26.07	26.20	26.80
Viaduct	25.86	27.03	27.26	27.74	27.93	28.12	28.21	28.01
AVG	27.30	28.40	28.61	28.99	29.17	29.21	29.38	29.99

Table 4. LPIPS results on the UCMerced LandUse dataset with scaling factors of ×2, ×3, and ×4

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Table 4. LPIPS results on the UCMerced LandUse dataset with scaling factors of ×2, ×3, and ×4

Scale	LPIPS
Scale	Bicubic	SRCNN	FSRCNN	VDSR	LGCNet	DCM	HSENet	TransENet	Ours
2	0.0721	0.0444	0.0471	0.0287	0.0293	0.0284	0.0266	0.0279	0.0256
3	0.1281	0.0945	0.1062	0.0801	0.0752	0.0698	0.0654	0.0649	0.0641
4	0.1650	0.1260	0.1395	0.1102	0.1093	0.1046	0.1081	0.1030	0.1022

Table 5. Ablation results of module structures
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Table 5. Ablation results of module structures
Model GISM CESSM CECSM PSNR/dB SSIM
Model 0 × × × 36.81 0.9609
Model 1 √ × × 36.86 0.9613
Model 2 √ √ × 36.90 0.9615
Model 3 (ours) √ √ √ 36.93 0.9617

Table 6. Ablation results of the multi-scale feedforward neural network (MSFFN)
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Table 6. Ablation results of the multi-scale feedforward neural network (MSFFN)
Method Params/M FLOPs/G PSNR/dB
$3 \times 3$ 1.98 126 36.91
$5 \times 5$ 2.08 140 36.88
$7 \times 7$ 2.35 157 36.90
MSFFN (ours) 2.13 147 36.93

Table 7. Comparison analysis of the multi-scale feedforward neural network (MSFFN) with other representative feedforward neural networks
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Table 7. Comparison analysis of the multi-scale feedforward neural network (MSFFN) with other representative feedforward neural networks
Method Param/M FLOPs/G PSNR/dB
MLP 1.96 120 36.85
Conv-FFN 2.02 131 36.88
GDFN 2.09 142 36.89
MSFFN (ours) 2.13 147 36.93

Table 8. Comparison analysis of ADAN with other representative CNN-Transformer architectures
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Table 8. Comparison analysis of ADAN with other representative CNN-Transformer architectures
Method PSNR/dB SSIM
TransENet 35.43 0.9355
Spatial dimension Transformer 35.52 0.9521
Frequency dimension Transformer 35.56 0.9602
ADAN(ours) 35.62 0.9719

Table 9. Model complexity analysis
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Table 9. Model complexity analysis
Method Param/M Flops/G PSNR/dB
LGCNet 0.193 7.11 33.48
DCM 2.180 7.32 33.65
HSENet 5.400 10.80 34.22
TransENet 37.800 9.32 35.43
ADAN(ours) 4.120 7.16 35.62

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Fei Wu, Jiacheng Chen, Jun Yang, Wanliang Wang, Guoqing Li. Remote-sensing images reconstruction based on adaptive dual-domain attention network[J]. Opto-Electronic Engineering, 2025, 52(4): 240297

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Paper Information

Category: Article

Received: Dec. 17, 2024

Accepted: Feb. 17, 2025

Published Online: Jun. 11, 2025

The Author Email: Jun Yang (杨俊)

DOI:10.12086/oee.2025.240297

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. PSNR/SSIM results on the UCMerced LandUse dataset (×2, ×3, and ×4)

Table 1. PSNR/SSIM results on the UCMerced LandUse dataset (×2, ×3, and ×4)

Table 2. PSNR/SSIM results on the AID dataset (×2, ×3, and ×4)

Table 2. PSNR/SSIM results on the AID dataset (×2, ×3, and ×4)

Table 3. Average PSNR for each category with an upscaling factor of ×4 on the AID dataset

Table 3. Average PSNR for each category with an upscaling factor of ×4 on the AID dataset

Table 4. LPIPS results on the UCMerced LandUse dataset with scaling factors of ×2, ×3, and ×4

Table 4. LPIPS results on the UCMerced LandUse dataset with scaling factors of ×2, ×3, and ×4

Table 5. Ablation results of module structures

Table 5. Ablation results of module structures

Table 6. Ablation results of the multi-scale feedforward neural network (MSFFN)

Table 6. Ablation results of the multi-scale feedforward neural network (MSFFN)

Table 7. Comparison analysis of the multi-scale feedforward neural network (MSFFN) with other representative feedforward neural networks

Table 7. Comparison analysis of the multi-scale feedforward neural network (MSFFN) with other representative feedforward neural networks

Table 8. Comparison analysis of ADAN with other representative CNN-Transformer architectures

Table 8. Comparison analysis of ADAN with other representative CNN-Transformer architectures

Table 9. Model complexity analysis

Table 9. Model complexity analysis