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Multi-Scale Dilated Convolution Neural Network for Image Artifact Correction of Limited-Angle Tomography
- Source :
- IEEE Access, Vol 8, Pp 1567-1576 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Limited-angle computed tomography (CT) has arisen in some medical and industrial applications. It is also a challenging problem since some scan views are missing and the directly reconstructed images often suffer from severe distortions. For such kind of problems, we analyze the features of limited-angle CT images and propose a multi-scale dilated convolution neural network (MSD-CNN) to correct the artifacts and to restore the image. In this network, the dilated convolution layer and multi-scale pooling layer are combined to form a group and exited in the whole encoder-decoder process. Since the dilated convolutions support an exponential expansion of the receptive field without losing resolution and coverage, the obtained artifact features possess the multi-scale characteristic. Furthermore, to improve the effectiveness and accuracy of the training step, we employ a preprocessing method, which extracts image patches. Numerical experiments verify the out-performance of the proposed method compared with some conventional methods, such as Unet based deep learning,TV- and L0 -based optimization methods.
- Subjects :
- 0209 industrial biotechnology
General Computer Science
Computer science
multi-scale
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Scale (descriptive set theory)
artifact correction
02 engineering and technology
Convolutional neural network
Image (mathematics)
Convolution
020901 industrial engineering & automation
0202 electrical engineering, electronic engineering, information engineering
Preprocessor
General Materials Science
Computer vision
Artifact (error)
business.industry
Deep learning
General Engineering
Process (computing)
dilated convolution
Limited-angle tomography
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....c5d2cf0c4e2b91390841e9095800e3b9