Neural Network-Based Noise Suppression Algorithm for Star Images Captured During Daylight Hours

Yuchen Liu; Chunhui Zhao; Qing Xu

doi:10.3788/AOS201939.0610003

Acta Optica Sinica, Volume. 39, Issue 6, 0610003(2019)

Neural Network-Based Noise Suppression Algorithm for Star Images Captured During Daylight Hours

Yuchen Liu^*, Chunhui Zhao, and Qing Xu

Author Affiliations

Beijing Institute of Control Engineering, China Academy of Space Technology, Beijing 100190, China

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Figures & Tables(14)

Fig. 1. Relationship between pixel response and different input parameters. (a) Solar azimuth; (b) altitude angle of observation

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Fig. 2. Schematic of network structure

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Fig. 3. Structural diagrams of different network shortcut connections. (a) Overall shortcut; (b) local shortcut

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Fig. 4. Convergence curves of network with different types of shortcut connection

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Fig. 5. Convergence curves of network with different functional structures

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Fig. 6. Processing results of different algorithms. (a) Original star image; (b) K-SVD; (c) BM3D; (d) DnCNN; (e) proposed method

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Fig. 7. Renderings of real star image after denoising and subtracting background by different algorithms. (a) Original image; (b) algorithm in Ref. [7]; (c) algorithm in Ref. [8]; (d) proposed algorithm

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Table 1. Example of input parameters for ModTran software
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Table 1. Example of input parameters for ModTran software
Latitude season Mid-latitude summer Latitude season Mid-latitude summer
Terrain Forest Altitude angle of observation ${μ_{1}}_{}$ /(°) 70
Weather Sunny Solar azimuth ${μ_{2}}_{}$ /(°) 90
Altitude H /km 8 Solar elevation θ /(°) 70

Table 2. Optical-system parameters of detector
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Table 2. Optical-system parameters of detector
Parameter Value Parameter Value
F /(°) 2 N_x× ${N_{y}}_{}$ /(pixel×pixel) 512×512
${S_{pix}}_{}$ /μm 11 f /mm 161.1
D /mm 41 τ 0.80
ζ 0.75 Q 0.40
d_w 120000e $σ_{PRNU}^{2}$ 0.0001
t /ms 10 n 3
${λ_{\min}}_{}$ /nm 800 I_min 400
${λ_{\max}}_{}$ /nm 1100 I_max 3600

Table 3. Input parameters of ModTran software

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Table 3. Input parameters of ModTran software

Latitude season	Mid-latitude summer	Latitude season	Mid-latitude summer
Terrain	Forest	Altitude angle of observation ${μ_{1}}_{}$ /(°)	50-90
Weather	Sunny	Solar azimuth ${μ_{2}}_{}$ /(°)	55-150
Altitude H /km	8	Solar elevation θ /(°)	70

Table 4. Running time of network with different functional structures
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Table 4. Running time of network with different functional structures
Types of convolutional network Dilated Conv Downsampling Conv Plain Conv
${t_{GPU}}_{}$ /ms 16.21 16.11 16.84
${t_{CPU}}_{}$ /s 0.87 0.22 0.86

Table 5. PSNR of different algorithms
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Table 5. PSNR of different algorithms
Algorithm PSNR /dB
Original star image 22.36
K-SVD 25.43
BM3D 26.15
DnCNN 37.42
Proposed algorithm 37.43

Table 6. Running time of different algorithms
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Table 6. Running time of different algorithms
Algorithm K-SVD BM3D DnCNN Proposed
${t_{GPU}}_{}$ /ms - - 45.43 16.84
${t_{CPU}}_{}$ /s 1.64 1.84 12.12 0.21

Table 7. Statistical RSN of star points after denoising by different algorithms
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Table 7. Statistical RSN of star points after denoising by different algorithms
Algorithm ${R_{SN}}_{}$ /dB
Original image 5.82
Algorithm in Ref. [8] 7.95
Algorithm in Ref. [7] 7.56
Proposed algorithm 195

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Yuchen Liu, Chunhui Zhao, Qing Xu. Neural Network-Based Noise Suppression Algorithm for Star Images Captured During Daylight Hours[J]. Acta Optica Sinica, 2019, 39(6): 0610003

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