基于多方向梯度网络的自适应边缘信息图像去噪模型

doi:10.13229/j.cnki.jdxbgxb.20231453

Abstract

Abstract:

To address the limitation that existing learning-based image denoising algorithms struggle to preserve edges and textures， we propose an adaptive edge-aware denoising model built upon a multi-directional gradient network that can capture distinct image information separately. First， multi-directional gradient operators are applied to the clean target image to generate noise-free gradient maps， which then guide the network in learning gradient representations free from corruption. Second， an adaptive gradient-fusion module is introduced to fuse gradient cues with the noisy image adaptively， increasing the network’s attention to edge and texture details. Experimental results demonstrate that the proposed model achieves competitive PSNR and SSIM values. Moreover， the denoised images consistently exhibit superior visual quality， underscoring the model’s potential for practical image-denoising applications.

Key words: deep neural network, image denoising, gradient operator, adaptive fusion

CLC Number:

TP391.4

Zi-tong WANG,Jing ZHAO,Shuang QIAO,Rui ZHU. Adaptive edge information image denoising model based on multi-directional gradient network[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(10): 3319-3328.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Table 1

Table 2

Table 3

Comparison of average PSNR（dB）/SSIM of different denoising algorithms on three standard libraries（σn=50）"

数据库	Set12		BSD68		Urban100
指标	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
DnCNN	27.18	0.782 9	26.23	0.718 9	26.28	0.787 4
FFDNet	27.32	0.790 3	26.29	0.724 5	26.52	0.805 7
MemNet	27.38	0.793 3	26.35	0.729 7	26.64	0.802 9
$N 3 N e t$	27.43	0.795 0	26.40	0.730 2	26.82	0.814 1
MWDCNN	27.36	0.790 4	26.40	0.729 7	27.12	0.806 3
LIGN	27.61	0.793 3	26.53	0.736 2	27.68	0.826 1
DRANet	27.62	0.800 3	26.47	0.731 6	27.90	0.829 4
本文算法	27.76	0.803 9	26.56	0.739 2	28.09	0.836 4

Table 3

Table 4

Comparison of single image PSNR（dB） of different denoising algorithms on the Set12 dataset（σn=50）"

算法	C.man	House	Peppers	Starfish	Monarch	Airplane	Parrot	Lena	Barbara	Boat	Man	Couple
DnCNN	27.03	30.00	27.32	25.70	26.78	25.87	26.48	29.39	26.22	27.20	27.24	26.90
FFDNet	27.05	30.37	27.54	25.75	26.81	25.89	26.57	29.66	26.45	27.33	27.29	27.08
MemNet	27.17	30.52	27.47	25.81	26.98	25.95	26.52	29.68	26.67	27.34	27.30	27.14
$N 3 N e t$	27.18	30.60	27.63	26.13	27.02	25.94	26.47	29.65	26.92	27.33	27.26	27.03
MWDCNN	27.15	30.56	27.44	25.82	27.00	25.83	26.53	29.64	26.67	27.32	27.22	27.08
LIGN	27.30	30.86	27.67	26.39	27.13	26.03	26.67	29.87	27.34	27.49	27.30	27.26
DRANet	27.58	30.89	27.62	25.99	27.13	26.03	26.67	29.59	27.34	27.57	27.34	27.36
本文算法	27.42	31.09	27.81	26.69	27.22	26.04	26.67	29.96	27.82	27.58	27.37	27.38

Table 4

Fig.4

Fig.5

Fig.6

Table 5

Comparison of different denoising network model parameter quantities and running time on noisy images of size 256×256， 512×512 and 1 024×1 024 and noise level 50"

尺寸/资源消耗	DnCNN	MemNet	FFDNet	$N 3 N e t$	DRANet	MWDCNN	LIGN	MDGAENet
Size 256×256	0.01	0.88	0.01	0.17	0.08	0.06	0.02	0.01
Size 512×512	0.05	3.61	0.03	0.74	0.38	0.22	0.06	0.12
Size 1 024×1 024	0.16	14.69	0.11	3.25	1.24	3.89	2.10	1.33
Memory cost/M	0.554	0.667	0.490	0.706	1.211	0.525	3.749	3.845

Table 5

Table 6

Fig.7

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数据库	Set12		BSD68		Urban100
指标	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
DnCNN	32.86	0.903 1	31.73	0.890 7	32.68	0.925 5
FFDNet	32.75	0.902 7	31.63	0.890 2	32.43	0.927 3
MemNet	32.84	0.904 4	31.70	0.892 7	32.84	0.926 1
N³Net	-	-	-	-	-	-
MWDCNN	32.87	0.907 2	31.87	0.897 6	33.15	0.931 3
LIGN	33.03	0.909 9	31.77	0.896 4	33.46	0.935 6
DRANet	33.00	0.905 6	31.79	0.892 1	33.55	0.932 8
本文算法	33.19	0.911 8	31.93	0.900 6	33.86	0.938 9

数据库	Set12		BSD68		Urban100
指标	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
DnCNN	30.44	0.862 2	29.23	0.828 7	29.97	0.879 7
FFDNet	30.43	0.863 4	29.19	0.828 9	29.92	0.888 6
MemNet	30.43	0.860 2	29.20	0.826 2	30.45	0.882 9
N³Net	30.50	0.865 1	29.30	0.832 9	30.19	0.891 0
WMDCNN	30.52	0.867 0	29.34	0.837 6	30.64	0.892 9
LIGN	30.72	0.871 1	29.25	0.833 4	31.04	0.901 3
DRANet	30.69	0.868 1	29.36	0.832 6	31.18	0.899 9
本文算法	30.83	0.873 0	29.48	0.842 4	31.39	0.906 3

	σ=50		σ=25		σ=15
评估指标	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
案例1	27.76	0.802 4	30.82	0.873 6	33.19	0.911 8
案例2	27.31	0.785 0	30.52	0.867 2	32.82	0.906 7
案例3	27.60	0.800 0	30.71	0.869 4	33.06	0.909 9

Adaptive edge information image denoising model based on multi-directional gradient network

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