CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation

doi:10.1109/ACCESS.2024.3373619

	CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation
	Haitong Tang 1; Shuang He 1; Mengduo Yang 2; Xia Lu 3; Qin Yu 4; Kaiyue Liu 1; Hongjie Yan 5; Nizhuan Wang1,6
	2024
发表期刊	IEEE ACCESS (IF:3.4[JCR-2023],3.7[5-Year])
ISSN	2169-3536
EISSN	2169-3536
卷号	12 页码:35844-35854
发表状态	已发表
DOI	10.1109/ACCESS.2024.3373619
摘要	It is still a challenging task to perform the semantic segmentation with high accuracy due to the complexity of real picture scenes. Many semantic segmentation methods based on traditional deep learning insufficiently captured the semantic and appearance information of images, which put limit on their generality and robustness for various application scenes. Thus, in this paper, we proposed a novel strategy that reformulated the popularly used convolution operation to multi-layer convolutional sparse coding block in semantic segmentation method to ease the aforementioned deficiency. To prove the effectiveness of our idea, we chose the widely used U-Net model for the demonstration purpose, and we designed CSC-Unet model series based on U-Net. Through extensive analysis and experiments, we provided credible evidence showing that the multi-layer convolutional sparse coding block enables semantic segmentation model to converge faster, extract finer semantic and appearance information of images, and improve the ability to recover spatial detail information. The best CSC-Unet model significantly outperforms the results of the original U-Net on three public datasets with different scenarios, i.e., 87.14%vs. 84.71%on DeepCrack dataset, 68.91%vs. 67.09%on Nuclei dataset, and 53.68%vs. 48.82%on CamVid dataset, respectively. In addition, the proposed strategy could be possibly used to significantly improve segmentation performance of any semantic segmentation model that involves convolution operations and the corresponding code is available at https://github.com/NZWANG/CSC-Unet. © 2013 IEEE.
关键词	U-Net semantic segmentation deep learning convolution operation convolutional sparse coding (CSC)
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20241115730343
EI主题词	Semantics
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 716.1 Information Theory and Signal Processing ; 723.4 Artificial Intelligence
原始文献类型	Journal article (JA)
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/354983
专题	生物医学工程学院信息科学与技术学院
作者单位	1.School of Geomatics and Marine Information, Jiangsu Ocean University, Lianyungang, China 2.School of Information Technology, Suzhou Institute of Trade and Commerce, Suzhou, China 3.School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou, China 4.School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China 5.Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, China 6.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
推荐引用方式 GB/T 7714	Haitong Tang,Shuang He,Mengduo Yang,et al. CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation[J]. IEEE ACCESS,2024,12:35844-35854.
APA	Haitong Tang.,Shuang He.,Mengduo Yang.,Xia Lu.,Qin Yu.,...&Nizhuan Wang.(2024).CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation.IEEE ACCESS,12,35844-35854.
MLA	Haitong Tang,et al."CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation".IEEE ACCESS 12(2024):35844-35854.