Researching | Deblurring Model of Image Multi-Scale Dense Network

Laser & Optoelectronics Progress, Volume. 56, Issue 21, 211001(2019)

Deblurring Model of Image Multi-Scale Dense Network

Haoze Song and Xiaojun Wu^*

Author Affiliations

School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China

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Figures&Tables (7)

References (45)

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Figures & Tables(7)

Fig. 1. Comparison of structures of Res block and Dense block

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Fig. 2. Structure of layers

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Fig. 3. Structure of network

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Fig. 4. Comparison of experiment results

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Fig. 5. Comparison of experiment results

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Table 1. Comparison of experimental results with GOPRO dataset
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Table 1. Comparison of experimental results with GOPRO dataset
Method Ref. [9] Ref. [14] Ref. [19]-wild Ref. [19] DN MSDN
PSNR 23.54 24.60 26.82 28.31 28.42 28.64
SSIM 0.8222 0.8420 0.8940 0.8880 0.8920 0.8950

Table 2. Comparison of experimental results with Kohler dataset
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Table 2. Comparison of experimental results with Kohler dataset
Method Ref. [9] Ref. [14] Ref. [19]-wild Ref. [19] DN MSDN
PSNR 24.62 25.22 26.10 25.35 25.72 26.12
SSIM 0.793 0.773 0.812 0.802 0.814 0.814

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Haoze Song, Xiaojun Wu. Deblurring Model of Image Multi-Scale Dense Network[J]. Laser & Optoelectronics Progress, 2019, 56(21): 211001

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Paper Information

Category: Image Processing

Received: Mar. 15, 2019

Accepted: Apr. 30, 2019

Published Online: Nov. 2, 2019

The Author Email: Xiaojun Wu (wu_xiaojun@jiangnan.edu.cn)

DOI:10.3788/LOP56.211001

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology