Researching | Millimeter-Wave Radiation Image Deblurring Based on Residual Recursive Network

Laser & Optoelectronics Progress, Volume. 59, Issue 18, 1810011(2022)

Millimeter-Wave Radiation Image Deblurring Based on Residual Recursive Network

Guohao Xu, Yuanyuan Liu^*, and Lu Zhu

Author Affiliations

School of Information Engineering, East China Jiaotong University, Nanchang 330013, Jangxi, China

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References (33)

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Figures & Tables(11)

Fig. 1. RSRN structure

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Fig. 2. Hierarchical blur extraction module structure

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Fig. 3. Structure of encoder LE

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Fig. 4. Internal structure of residual block (Res)

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Fig. 5. Structure of decoder LD

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Fig. 6. Internal structure of MSEB module

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Fig. 7. Some millimeter wave radiation image samples in dataset

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Fig. 8. Blur effect display

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Fig. 9. Visual comparison

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Table 1. Number of images in training set and test set
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Table 1. Number of images in training set and test set
Dataset Original dataset Preprocessed dataset
Number of images in training set 2979 5958
Number of images in test set 330 660
Total number of images 3309 6618

Table 2. Comparison of performance of different methods
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Table 2. Comparison of performance of different methods
Method PSNR SSIM Param /MB
SRN［^13］ 39.49 0.98738 3.76
MTRNN［^15］ 39.23 0.98737 2.64
SIUN^［32］ 35.77 0.98191 24.5
MPRNet^［33］ 35.67 0.98007 20.1
Proposed method 39.69 0.98774 2.56

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Guohao Xu, Yuanyuan Liu, Lu Zhu. Millimeter-Wave Radiation Image Deblurring Based on Residual Recursive Network[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1810011

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Paper Information

Category: Image Processing

Received: Jun. 9, 2021

Accepted: Jul. 28, 2021

Published Online: Aug. 22, 2022

The Author Email: Yuanyuan Liu (lyy.78@163.com)

DOI:10.3788/LOP202259.1810011

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology