Multiscale dense interactive attention residual real image denoising network

doi:10.11996/JG.j.2095-302X.2025020279

Abstract

Abstract:

To address the problem that the generated image is not clear enough due to incomplete feature extraction and low feature utilization in image denoising, a multi-scale dense interactive attention residual denoising network (MDIARN) was proposed. First, a multi-scale asymmetric feature extraction module (MAFM) was employed to preliminarily extract shallow information features, ensuring diversity of image features. Then, a multi-scale cascade module (MSCM) utilized multi-dimensional dense interactive residual units (MDIU) to perform multi-dimensional mapping of image features. These units were progressively cascaded to enhance the information transmission and interaction between models, fully fitting the training data. A dual-path global attention module (DGAM) was introduced to conduct global joint learning on multi-level features, acquiring more discriminative feature information. Skip connections were integrated to encourage parameter sharing between structures, enabling full integration of features from different dimensions and preserving the completeness of information. Finally, residual learning was employed to construct a clear denoised image. Experimental results demonstrated that this algorithm achieved peak signal-to-noise ratios of 39.80 dB and 39.62 dB on the real noise datasets (DND and SIDD), respectively, and structural similarities of 95.4% and 95.8%, respectively, outperforming mainstream denoising algorithms. In addition, the proposed algorithm demonstrated excellent performance in low-light environments, preserving more details and significantly enhancing image quality.

Key words: image denoising, multi-scale feature extraction, multi-dimensional dense interaction, convolutional neural network, attention

CLC Number:

TP391

GUO Yecai, HU Xiaowei, AMITAVE Saha, MAO Xiangnan. Multiscale dense interactive attention residual real image denoising network[J]. Journal of Graphics, 2025, 46(2): 279-287.

Figures/Tables 15

Fig. 1 Overall block diagram of MDIARN

Fig. 2 Multi-scale asymmetric feature extraction module((a) MAFM enhanced skeleton feature detail operation; (b) Detailed structure of MAFM)

Fig. 3 Detailed structure of the MSCM

Fig. 4 Detailed structure of the DGAM

Table 1 Environment configuration

名称	配置
Operating system	Windows 10
GPU	NVIDIA GeForce GTX 3060 Ti
CPU	Intel Core i5- 12400K @3.40 GHz
Deep learning framework	PyTorch 1.7.1+cu 101
Python Version	3.7

Table 2 Comparison results of different algorithms for denoising on SIDD and DND datasets

算法	SIDD		DND
算法	PSNR/dB	SSIM/%	PSNR/dB	SSIM/%
BM3D	26.65	68.5	34.51	85.1
RIDNet	38.73	95.4	39.25	95.0
AINDNet	38.96	95.2	39.37	95.1
MSGAN	39.11	95.5	39.59	95.5
DCBDNet	38.94	95.3	39.37	95.1
DCANet	39.27	95.6	39.57	95.3
MRIDN	39.43	95.7	39.49	95.1
Ours	39.62	95.8	39.80	95.4

Fig. 5 Comparison of denoising effects of different algorithms on the SIDD dataset ((a) Noisy image; (b) BM3D; (c) RIDNet; (d) DCBDNet; (e) DCANAet; (f) MRIDN; (g) Ours; (h) Clean image)

Fig. 6 Comparison of denoising effects of different algorithms on the DND dataset ((a) Noisy image; (b) BM3D; (c) RIDNet; (d) DCBDNet; (e) DCANAet; (f) MSGAN; (g) MRIDN; (h) Ours)

Fig. 7 Comparison of denoising effects of different algorithms on the MIDD dataset ((a) Noisy image; (b) BM3D; (c) RIDNet; (d) DCBDNet; (e) DCANAet; (f) MSGAN; (g) Ours; (h) Clean image)

Fig. 8 Comparison of denoising effects of different algorithms on images taken by cell phones ((a) Noisy image; (b) BM3D; (c) RIDNet; (d) DCBDNet; (e) AINDNet; (f) DCANAet; (g) MSGAN; (h) Ours)

Table 3 Convolutional kernel performance comparison

模块	类型	参数/M	PSNR/dB	SSIM/%
MAFM	非对称	9.67	39.62	95.8
3×3 Conv	对称	9.60	39.53	95.5

Table 4 Results of ablation experiments

变体	模块				PSNR/dB	SSIM/%
变体	MAFM	MSCM	DGAM	Skip connection	PSNR/dB	SSIM/%
MDIARN-a	√	-	-	-	35.36	91.0
MDIARN-b	√	√	-	-	39.45	95.4
MDIARN-c	√	-	√	-	37.53	94.0
MDIARN-d	√	√	√	-	39.54	95.7
MDIARN-e	√	√	-	√	39.53	95.7
Ours	√	√	√	√	39.62	95.8

Table 5 Performance comparison of the number of MSCM

数量	参数/M	PSNR/dB	SSIM/%
1	2.68	39.25	95.5
2	5.01	39.43	95.7
3	7.34	39.50	95.7
4	9.67	39.62	95.8
5	12.00	39.64	95.8

Table 6 Performance comparison of the number of convolutions in MDIU

数量	参数/M	PSNR/dB	SSIM/%
3	4.14	39.39	95.3
4	6.66	39.53	95.5
5	9.67	39.62	95.8
6	13.28	39.63	95.8
7	17.47	39.64	95.8

Table 7 Runtimes and model parameters for different models on a 256×256 noisy image

方法	时间/s	参数/M
BM3D	2.165(CPU)	-
RIDNet	0.046(GPU)	1.50
DCBDNet	0.054(GPU)	1.10
DCANet	0.059(GPU)	1.40
AINDNet	0.085(GPU)	13.76
MRIDN	0.051(GPU)	6.92
Ours	0.048(GPU)	9.67

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