Image denoising network based on local and global feature decoupling

doi:10.11772/j.issn.1001-9081.2023081131

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (8): 2571-2579.DOI: 10.11772/j.issn.1001-9081.2023081131

• Multimedia computing and computer simulation • Previous Articles Next Articles

Image denoising network based on local and global feature decoupling

Yuwei DING¹, Hongbo SHI¹(), Jie LI¹^,², Min LIANG¹

^1.School of Information，Shanxi University of Finance and Economics，Taiyuan Shanxi 030006，China
^2.Shanxi Provincial Key Laboratory of Economic Big Data （ Shanxi University of Finance and Economics），Taiyuan Shanxi 030006，China

Received:2023-08-23 Revised:2023-11-01 Accepted:2023-11-14 Online:2024-08-22 Published:2024-08-10
Contact: Hongbo SHI
About author:DING Yuwei， born in 1998， M. S. candidate. His research interests include machine learning， image processing.
LI Jie， born in 1986， Ph. D.， associate professor. His research interests include computer vision， image processing.
LIANG Min， born in 1979， Ph. D.， associate professor. Her research interests include image processing， pattern recognition.
Supported by:
Central Guiding Local Science and Technology Development Fund(YDZJSX20231A057);Key Research and Development Program of Shanxi Province(201903D121160);Natural Science Foundation of Shanxi Province(202203021211333);Graduate Research Innovation Project of Shanxi Province(2023KY505)

基于局部和全局特征解耦的图像去噪网络

丁宇伟¹, 石洪波¹(), 李杰¹^,², 梁敏¹

^1.山西财经大学信息学院，太原 030006
^2.经济大数据山西省重点实验室（山西财经大学），太原 030006

通讯作者: 石洪波
作者简介:丁宇伟（1998—），男，山西忻州人，硕士研究生，CCF会员，主要研究方向：机器学习、图像处理
石洪波（1965—），女，山西太原人，教授，博士，主要研究方向：机器学习、数据挖掘 shihb@sxufe.edu.cn
李杰（1986—），男，四川邛崃人，副教授，博士，主要研究方向：计算机视觉、图像处理
梁敏（1979—），女，山西忻州人，副教授，博士，主要研究方向：图像处理、模式识别。
基金资助:
中央引导地方科技发展资金资助项目(YDZJSX20231A057);山西省重点研发计划项目(201903D121160);山西省自然科学基金资助项目(202203021211333);山西省研究生科研创新项目(2023KY505)

Abstract

Abstract:

Concerning the problem that current Transformer-based algorithms focus on capturing the global features of images， but ignore the key role of local features to restore image details， an image denoising network based on local and global feature decoupling was proposed. The proposed network included two multi-scale branches based on Hybrid Transformer Block （HTB） and a single-scale branch based on Convolutional Neural Network （CNN）， aiming at combining powerful global modeling capability of HTB with local modeling advantage of HTB， and yielding outputs with enriched contextual information and precise spatial details. Within the HTB， self-attention mechanism was employed to adaptively model spatial- and channel-dimensional dependencies， activating a wider range of input pixels for reconstruction. Given the potential information conflicts across different branches， feature transfer block was designed to facilitate cross-branch propagation of global features and suppress low-frequency information， thereby ensuring collaborative interactions among the branches. Experimental results showed that： on the real-world image dataset SIDD， compared with Transformer-based denoising network Uformer， the proposed network improved Peak Signal-to-Noise Ratio （PSNR） by 0.09 dB and Structural SIMilarity （SSIM） by 0.001； on the synthetic image dataset Urban100， compared with multi-stage denoising network MSPNet （Multi-Stage Progressive denoising Network）， the average PSNR of the proposed network was improved by 0.41 dB. It can be seen that the proposed network effectively removes image noise and reconstructs finer texture details.

Key words: Transformer, image denoising, global feature, local feature, feature decoupling

摘要：

针对当前基于Transformer的图像去噪算法侧重于捕获图像的全局特征，而忽视局部特征对于恢复图像细节关键作用的问题，提出一种基于局部和全局特征解耦的图像去噪网络。该网络包含2个基于混合Transformer模块（HTB）的多尺度分支和1个基于卷积神经网络（CNN）的单尺度分支，旨在将HTB强大的全局建模能力与CNN的局部建模优势有机结合，生成上下文信息丰富且空间细节准确的输出。HTB采用自注意力机制自适应地对空间和通道维度的依赖关系建模，以激活范围更广的输入像素进行重建。鉴于不同分支间可能存在的信息冲突，设计特征传递模块，通过跨分支传递全局特征并抑制低频信息，从而确保各分支间的协同作用。实验结果表明，在真实世界图像数据集SIDD上，与基于Transformer的去噪网络Uformer相比，所提网络的峰值信噪比（PSNR）提高了0.09 dB，结构相似度（SSIM）提高了0.001；在合成图像数据集Urban100上，与多阶段去噪网络MSPNet（Multi-Stage Progressive denoising Network）相比，所提网络的平均PSNR提高了0.41 dB。可见，所提网络能有效去除图像噪声，并重建出更精细的纹理细节。

关键词: Transformer, 图像去噪, 全局特征, 局部特征, 特征解耦

CLC Number:

TP391.4

Yuwei DING, Hongbo SHI, Jie LI, Min LIANG. Image denoising network based on local and global feature decoupling[J]. Journal of Computer Applications, 2024, 44(8): 2571-2579.

丁宇伟, 石洪波, 李杰, 梁敏. 基于局部和全局特征解耦的图像去噪网络[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2571-2579.

Figures/Tables 15

Fig. 1 Overall structure of LGDNet

Fig. 2 Self-attention mechanism

Fig. 3 Channel attention block

Tab. 1 Denoising results of different methods on real-world image datasets

方法	DND		SIDD
方法	PSNR/dB	SSIM	PSNR/dB	SSIM
CBM3D^［2］	34.51	0.851	25.65	0.685
DnCNN^［4］	32.43	0.790	23.66	0.583
CBDNet^［31］	38.06	0.942	30.78	0.801
RIDNet^［5］	39.26	0.953	38.71	0.951
MIRNet^［6］	39.88	0.956	39.72	0.959
MPRNet^［19］	39.80	0.954	39.71	0.958
MSANet^［32］	39.65	0.955	39.56	0.912
MSPNet^［9］	39.75	0.954	39.78	0.959
MIRNet-v2^［10］	39.86	0.955	39.84	0.959
Uformer^［14］	39.96	0.956	39.77	0.959
ADFNet^［33］	39.87	0.955	39.79	0.960
LGDNet	40.10	0.956	39.86	0.960

Fig. 4 Denoising visualization results of different methods on DND dataset

Tab. 2 Denoising results of different methods on synthetic color image datasets

方法	CBSD68			Urban100			Kodak24
方法	$σ = 30$	$σ = 50$	$σ = 70$	$σ = 30$	$σ = 50$	$σ = 70$	$σ = 30$	$σ = 50$	$σ = 70$
CBM3D^［2］	29.73	27.38	26.00	30.36	27.95	26.31	30.89	28.63	27.27
DnCNN^［4］	30.40	28.01	26.56	30.28	28.16	26.17	31.39	29.16	27.64
RDN^［18］	30.67	28.31	26.85	31.69	29.29	27.63	31.94	29.66	28.20
MSPNet^［9］	30.76	28.47	27.03	31.64	29.40	27.66	31.99	29.74	28.34
MDRN^［34］	30.61	28.27	26.82	31.41	29.00	27.37	31.73	29.44	28.01
MSANet^［32］	30.67	28.36	29.96	N/A	N/A	N/A	31.78	29.57	28.17
PANet^［35］	30.70	28.33	26.89	31.87	29.47	27.87	31.96	29.65	28.20
LGDNet	30.79	28.50	27.04	31.98	29.78	28.05	32.02	29.76	28.37

Tab. 2 Denoising results of different methods on synthetic color image datasets

方法	CBSD68			Urban100			Kodak24
方法	$σ = 30$	$σ = 50$	$σ = 70$	$σ = 30$	$σ = 50$	$σ = 70$	$σ = 30$	$σ = 50$	$σ = 70$
CBM3D^［2］	29.73	27.38	26.00	30.36	27.95	26.31	30.89	28.63	27.27
DnCNN^［4］	30.40	28.01	26.56	30.28	28.16	26.17	31.39	29.16	27.64
RDN^［18］	30.67	28.31	26.85	31.69	29.29	27.63	31.94	29.66	28.20
MSPNet^［9］	30.76	28.47	27.03	31.64	29.40	27.66	31.99	29.74	28.34
MDRN^［34］	30.61	28.27	26.82	31.41	29.00	27.37	31.73	29.44	28.01
MSANet^［32］	30.67	28.36	29.96	N/A	N/A	N/A	31.78	29.57	28.17
PANet^［35］	30.70	28.33	26.89	31.87	29.47	27.87	31.96	29.65	28.20
LGDNet	30.79	28.50	27.04	31.98	29.78	28.05	32.02	29.76	28.37

Fig. 5 Denoising visualization results of different methods on Kodak24 dataset

Tab. 3 Denoising results of different methods on synthetic grayscale image datasets

方法	BSD68			Urban100			Kodak24
方法	$σ = 30$	$σ = 50$	$σ = 70$	$σ = 30$	$σ = 50$	$σ = 70$	$σ = 30$	$σ = 50$	$σ = 70$
BM3D^［3］	27.76	25.62	24.44	28.75	25.95	24.27	29.13	26.99	25.73
DnCNN^［4］	28.36	26.23	24.90	28.88	26.28	24.36	29.62	27.51	26.08
RNAN^［36］	28.61	26.48	25.18	30.20	27.65	25.89	30.04	27.93	26.60
RDN^［18］	28.58	26.43	25.12	30.08	27.47	25.71	30.02	27.88	26.57
MDRN^［34］	N/A	26.44	N/A	N/A	27.31	N/A	N/A	N/A	N/A
MSPNet^［9］	28.64	26.55	25.31	30.09	27.64	25.98	30.06	28.01	26.59
MSANet^［32］	28.61	26.51	25.25	N/A	N/A	N/A	29.91	27.81	26.54
LGDNet	28.68	26.58	25.31	30.45	28.14	26.44	30.08	28.04	26.77

Tab. 3 Denoising results of different methods on synthetic grayscale image datasets

方法	BSD68			Urban100			Kodak24
方法	$σ = 30$	$σ = 50$	$σ = 70$	$σ = 30$	$σ = 50$	$σ = 70$	$σ = 30$	$σ = 50$	$σ = 70$
BM3D^［3］	27.76	25.62	24.44	28.75	25.95	24.27	29.13	26.99	25.73
DnCNN^［4］	28.36	26.23	24.90	28.88	26.28	24.36	29.62	27.51	26.08
RNAN^［36］	28.61	26.48	25.18	30.20	27.65	25.89	30.04	27.93	26.60
RDN^［18］	28.58	26.43	25.12	30.08	27.47	25.71	30.02	27.88	26.57
MDRN^［34］	N/A	26.44	N/A	N/A	27.31	N/A	N/A	N/A	N/A
MSPNet^［9］	28.64	26.55	25.31	30.09	27.64	25.98	30.06	28.01	26.59
MSANet^［32］	28.61	26.51	25.25	N/A	N/A	N/A	29.91	27.81	26.54
LGDNet	28.68	26.58	25.31	30.45	28.14	26.44	30.08	28.04	26.77

Fig. 6 Denoising visualization results of different methods on BSD68 dataset

Fig. 7 Comprehensive performance test results of different branches of model

Fig. 8 Denoising visualization results of different branches

Tab. 4 Effects of different modules on denoising performance

模型	S-A	C-A	多阶段	Local	FTB	PSNR/dB
$M 0$	$×$	$√$	$×$	$×$	$×$	29.26
$M 1$	$√$	$×$	$×$	$×$	$×$	29.30
$M 2$	$√$	$√$	$×$	$×$	$×$	26.36
$M 3$	$√$	$√$	$√$	$×$	$×$	26.38
$M 4$	$√$	$√$	$√$	$√$	$×$	26.42
LGDNet	$√$	$√$	$√$	$√$	$√$	26.44

Tab. 4 Effects of different modules on denoising performance

模型	S-A	C-A	多阶段	Local	FTB	PSNR/dB
$M 0$	$×$	$√$	$×$	$×$	$×$	29.26
$M 1$	$√$	$×$	$×$	$×$	$×$	29.30
$M 2$	$√$	$√$	$×$	$×$	$×$	26.36
$M 3$	$√$	$√$	$√$	$×$	$×$	26.38
$M 4$	$√$	$√$	$√$	$√$	$×$	26.42
LGDNet	$√$	$√$	$√$	$√$	$√$	26.44

Tab. 5 Performance comparison of different branches and module combinations

模型	分支数	网络类型	HTB	CAB	PSNR/dB
$B 0$	1	U-Net	$√$	$×$	26.12
$B 1$	1	Local	$√$	$×$	26.05
$B 2$	2	U-Nets	$√$	$√$	26.27
$B 3$	2	U-Net+Local	$√$	$√$	26.30
$B 4$	3	U-Net+Locals	$√$	$√$	26.39
$B 5$	3	U-Nets+Local	$√$	$√$	26.44

Tab. 5 Performance comparison of different branches and module combinations

模型	分支数	网络类型	HTB	CAB	PSNR/dB
$B 0$	1	U-Net	$√$	$×$	26.12
$B 1$	1	Local	$√$	$×$	26.05
$B 2$	2	U-Nets	$√$	$√$	26.27
$B 3$	2	U-Net+Local	$√$	$√$	26.30
$B 4$	3	U-Net+Locals	$√$	$√$	26.39
$B 5$	3	U-Nets+Local	$√$	$√$	26.44

Fig. 9 Ablation study of different parameter settings in LGDNet

Tab. 6 Comprehensive performance comparison of different denoising methods

方法	参数量/10⁶	FLOPs/10⁹	PSNR/dB
MSPNet^［9］	54.6	298.0	39.75
MIRNet^［6］	31.8	1 572.0	39.88
MPRNet^［19］	20.1	1 176.0	39.80
SwinIR^［12］	11.8	788.6	40.01
Restormer^［13］	26.1	155.0	40.03
MAXIM^［37］	22.2	339.2	39.84
LGDNet	18.3	340.2	40.10

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Image denoising network based on local and global feature decoupling

基于局部和全局特征解耦的图像去噪网络

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