DnRFD：Progressive Residual Fusion Dense Network for Image Denoising

doi:10.3778/j.issn.1673-9418.2103030

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (12): 2841-2850.DOI: 10.3778/j.issn.1673-9418.2103030

• Graphics and Image • Previous Articles Next Articles

DnRFD：Progressive Residual Fusion Dense Network for Image Denoising

CAO Yiqin¹, RAO Zhechu¹, ZHU Zhiliang¹^,²^,⁺(), ZHANG Hongbin¹

1. School of Software, East China Jiaotong University, Nanchang 330013, China
2. Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

Received:2021-03-10 Revised:2021-04-27 Online:2022-12-01 Published:2021-04-30
About author:CAO Yiqin, born in 1964, M.S., professor, member of CCF. His research interests include image processing and pattern recognition.
RAO Zhechu, born in 1997, M.S. candidate. His research interest is image processing.
ZHU Zhiliang, born in 1988, Ph.D., lecturer, member of CCF. His research interests include image information processing, virtual reality and human-computer interaction.
ZHANG Hongbin, born in 1979, Ph.D., associate professor, member of CCF. His research interests include computer vision, natural language processing and recommendation systems.
Supported by:
National Natural Science Foundation of China(61861016);Key Project of Science and Technology Support Plan of Jiangxi Province(20161BBE50081);Youth Science Foundation of Jiangxi Province(20202BABL212006);Science and Technology Research Project of Education Department of Jiangxi Province(GJJ190359)

DnRFD：用于图像去噪的递进式残差融合密集网络

曹义亲¹, 饶哲初¹, 朱志亮¹^,²^,⁺(), 张红斌¹

1.华东交通大学软件学院，南昌 330013
2.中国科学院软件研究所，北京 100190

通讯作者: +E-mail: rj_zzl@ecjtu.edu.cn
作者简介:曹义亲（1964—），男，江西九江人，硕士，教授，CCF会员，主要研究方向为图像处理、模式识别。
饶哲初（1997—），男，江西丰城人，硕士研究生，主要研究方向为图像处理。
朱志亮（1988—），男，湖北天门人，博士，讲师，CCF会员，主要研究方向为图像信息处理、虚拟现实、人机交互。
张红斌（1979—），男，江苏如皋人，博士，副教授，CCF会员，主要研究方向为计算机视觉、自然语言处理、推荐系统。
基金资助:
国家自然科学基金(61861016);江西省科技支撑计划重点项目(20161BBE50081);江西省青年科学基金项目(20202BABL212006);江西省教育厅科学技术研究项目(GJJ190359)

Abstract

Abstract:

The denoising method based on deep learning can achieve better denoising effect than the traditional method, but the existing deep learning denoising methods often have the problem of excessive computational complexity caused by too deep network. To solve this problem, a progressive residual fusion dense network (DnRFD) is proposed to remove Gaussian noise. Firstly, dense blocks are used to learn the noise distribution in the image, and the network parameters are greatly reduced while the local features of the image are fully extracted. Then, a progressive strategy is used to connect the shallow convolution features with the deep features to form a residual fusion network to extract more global features for noise. Finally, the output characteristic images of each dense block are fused and input to the reconstructed output layer to get the final output result. Experimental results show that, when the Gaussian white noise level is 25 and 50, the network can achieve higher mean PSNR and mean structural similarity, and the average time of denoising is half of the DnCNN method and one third of the FFDNet method. In general, the overall denoising performance of the network is better than that of the correlative comparison algorithms, and it can effectively remove the white Gaussian noise and natural noise in the image, and can restore the edge and texture details of the image better.

Key words: image denoising, deep learning, dense block, residual learning, progressive residual fusion

摘要：

基于深度学习的去噪方法能够获得比传统方法更好的去噪效果，但是现有的深度学习去噪方法往往存在网络过深导致计算复杂度过大的问题。针对这个不足，提出一种用于去除高斯噪声的递进式残差融合密集网络（DnRFD）。该网络首先采用密集块来学习图像中的噪声分布，在充分提取图像局部特征的同时大幅降低网络参数；然后利用递进策略将浅层卷积特征依次与深层特征短线连接形成残差融合网络，提取出更多针对噪声的全局特征；最后将各密集块的输出特征图进行融合后输入给重建输出层，得到最后的输出结果。实验结果表明，在高斯白噪声等级为25和50时，该网络都能获得较高的峰值信噪比均值和结构相似性均值，并且去噪平均时间是DnCNN方法的一半，是FFDNet方法的1/3。总的来说，该网络整体去噪性能优于相关对比算法，可有效去除图像中的高斯白噪声和自然噪声，同时能更好地还原图像边缘与纹理细节。

关键词: 图像去噪, 深度学习, 密集块, 残差学习, 递进式残差融合

CLC Number:

TP391

CAO Yiqin, RAO Zhechu, ZHU Zhiliang, ZHANG Hongbin. DnRFD：Progressive Residual Fusion Dense Network for Image Denoising[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(12): 2841-2850.

曹义亲, 饶哲初, 朱志亮, 张红斌. DnRFD：用于图像去噪的递进式残差融合密集网络[J]. 计算机科学与探索, 2022, 16(12): 2841-2850.

Figures/Tables 17

Fig.1 Dense Block modular structure

Fig.2 Bottleneck structure

Fig.3 Whole structure of progressive residual fusion dense network

Fig.4 Overall framework of image denoising method based on RFD

Fig.5 Original test images for low level noise denoising

Table 1 PSNR results obtained by different algorithms (noise level is 25)

Methods	House	Pepper	Ship	Man	Landscape	Airplane	Average
NLM^[5]	30.532	28.380	27.921	27.996	30.661	35.476	30.161
BM3D^[6]	32.860	30.162	29.910	29.623	32.542	36.932	32.004
WNNM^[8]	33.230	30.400	30.030	29.770	32.710	37.260	32.233
DnCNN^[12]	32.930	30.660	30.150	30.030	33.110	38.270	32.525
FFDNet^[21]	33.060	30.720	30.210	30.040	33.130	38.320	32.580
Ours	33.083	30.799	30.222	30.049	33.198	38.389	32.623

Table 2 SSIM results obtained by different algorithms (noise level is 25)

Methods	House	Pepper	Ship	Man	Landscape	Airplane	Average
NLM^[5]	0.820	0.820	0.730	0.740	0.820	0.900	0.805
BM3D^[6]	0.860	0.870	0.800	0.810	0.890	0.950	0.863
WNNM^[8]	0.861	0.870	0.802	0.810	0.890	0.960	0.866
DnCNN^[12]	0.961	0.973	0.951	0.940	0.960	0.920	0.951
FFDNet^[21]	0.862	0.879	0.812	0.822	0.896	0.969	0.873
Ours	0.962	0.976	0.954	0.942	0.963	0.940	0.956

Table 3 Processing time of different algorithms (noise level is 25)

Methods	$256 × 256$		$256 × 256$	$512 × 512$		$512 × 512$	$418 × 321$		$418 × 321$	Overall mean
Methods	House	Pepper	Mean	Ship	Man	Mean	Landscape	Airplane	Mean	Overall mean
NLM^[5]	0.051	0.034	0.043	0.166	0.157	0.162	0.080	0.094	0.087	0.097
BM3D^[6]	1.000	0.800	0.900	3.600	3.600	3.600	2.200	2.300	2.250	2.250
WNNM^[8]	134.925	131.224	133.075	552.875	596.001	574.438	361.181	323.310	342.246	349.920
DnCNN^[12]	0.176	0.173	0.175	0.678	0.668	0.673	0.417	0.405	0.411	0.419
FFDNet^[21]	0.480	0.336	0.408	1.107	0.904	1.006	0.527	0.499	0.513	0.642
Ours	0.099	0.087	0.093	0.398	0.381	0.389	0.238	0.218	0.228	0.237

Table 3 Processing time of different algorithms (noise level is 25)

Methods	$256 × 256$		$256 × 256$	$512 × 512$		$512 × 512$	$418 × 321$		$418 × 321$	Overall mean
Methods	House	Pepper	Mean	Ship	Man	Mean	Landscape	Airplane	Mean	Overall mean
NLM^[5]	0.051	0.034	0.043	0.166	0.157	0.162	0.080	0.094	0.087	0.097
BM3D^[6]	1.000	0.800	0.900	3.600	3.600	3.600	2.200	2.300	2.250	2.250
WNNM^[8]	134.925	131.224	133.075	552.875	596.001	574.438	361.181	323.310	342.246	349.920
DnCNN^[12]	0.176	0.173	0.175	0.678	0.668	0.673	0.417	0.405	0.411	0.419
FFDNet^[21]	0.480	0.336	0.408	1.107	0.904	1.006	0.527	0.499	0.513	0.642
Ours	0.099	0.087	0.093	0.398	0.381	0.389	0.238	0.218	0.228	0.237

Fig.6 Performance comparison of different algorithms on House image (noise level is 25)

Fig.7 Performance comparison of different algorithms on Man image (noise level is 25)

Fig.8 Original test images for high level noise denoising

Table 4 PSNR results obtained by different algorithms (noise level is 50)

Methods	Starfish	Butterfly	Lena	Room	Desert	Coral	Average
NLM^[5]	22.843	23.853	26.643	24.048	27.211	24.549	24.858
BM3D^[6]	24.842	25.662	28.962	21.099	28.483	25.674	25.785
WNNM^[8]	25.430	26.320	29.250	26.640	28.560	25.920	27.020
DnCNN^[12]	25.703	26.871	29.435	26.884	28.833	26.211	27.323
FFDNet^[21]	25.680	26.920	29.439	27.040	28.840	26.290	27.368
Ours	25.706	26.932	29.452	27.094	28.854	26.303	27.390

Table 5 SSIM results obtained by different algorithms (noise level is 50)

Methods	Starfish	Butterfly	Lena	Room	Desert	Coral	Average
NLM^[5]	0.630	0.720	0.680	0.570	0.620	0.600	0.637
BM3D^[6]	0.740	0.820	0.810	0.710	0.730	0.690	0.751
WNNM^[8]	0.760	0.830	0.810	0.710	0.720	0.690	0.753
DnCNN^[12]	0.930	0.950	0.920	0.900	0.850	0.910	0.911
FFDNet^[21]	0.775	0.858	0.821	0.734	0.731	0.727	0.774
Ours	0.932	0.953	0.921	0.920	0.860	0.912	0.916

Fig.9 Performance comparison of different algorithms on Room image (noise level is 50)

Fig.10 Performance comparison of different algorithms on Lena image (noise level is 50)

Fig.11 Performance of proposed algorithm on natural images

Fig.12 Performance of proposed algorithm on color images

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DnRFD：Progressive Residual Fusion Dense Network for Image Denoising

DnRFD：用于图像去噪的递进式残差融合密集网络

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