Researching | Micro-CT Image Denoising Algorithm Based on Deep Residual Encoding-Decoding

Laser & Optoelectronics Progress, Volume. 60, Issue 14, 1410014(2023)

Micro-CT Image Denoising Algorithm Based on Deep Residual Encoding-Decoding

Huijuan Fu¹, Xiaoqi Xi¹, Yu Han¹, Lei Li¹, Xinguang Wang², and Bin Yan^1、*

Author Affiliations

¹College of Information System Engineering, Information Engineering University, Zhengzhou 450001, Henan, China

²Henan Provincial Institute of Cultural Heritage and Archaeology, Zhengzhou 450001, Henan, China

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Fig. 1. DRED-CNN structure

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Fig. 2. Composition of convolution block and deconvolution block

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Fig. 3. Residual learning network structure

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Fig. 4. Composition of lab-level micro-CT system

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Fig. 5. Denoising results of different methods for bronze coins

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Fig. 6. Denoising results of different methods for bronze residual coins

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Table 1. Scanning parameters for bronze coins
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Table 1. Scanning parameters for bronze coins
SOD /mm SDD /mm Voxel size /μm Tube voltage /kV Power /W Time of exposure /s
80.01 200.01 27.26 140 10 3.0/0.1

Table 2. Reconstruction quality evaluation of different methods for images
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Table 2. Reconstruction quality evaluation of different methods for images
Method Average PSNR Average SSIM
LDCT 33.8335 0.9163
BM3D 34.1415 0.9508
Multiscale-RED 35.0793 0.9424
RED-CNN 35.8979 0.9644
DRED-CNN 38.8658 0.9745

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Huijuan Fu, Xiaoqi Xi, Yu Han, Lei Li, Xinguang Wang, Bin Yan. Micro-CT Image Denoising Algorithm Based on Deep Residual Encoding-Decoding[J]. Laser & Optoelectronics Progress, 2023, 60(14): 1410014

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Paper Information

Category: Image Processing

Received: Jun. 6, 2022

Accepted: Sep. 13, 2022

Published Online: Jul. 17, 2023

The Author Email: Yan Bin (ybspace@hotmail.net)

DOI:10.3788/LOP221785

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology