Improved Super-Resolution Image Reconstruction Algorithm

Haicheng Qu; Bowen Tang; Guisen Yuan

doi:10.3788/LOP202158.0210018

Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 2 >Page 0210018 > Article

Laser & Optoelectronics Progress
Vol. 58, Issue 2, 0210018 (2021)

Improved Super-Resolution Image Reconstruction Algorithm

Haicheng Qu^*, Bowen Tang^*, and Guisen Yuan^*

Author Affiliations

School of Software, Liaoning Technical University, Huludao, Liaoning 125105, China

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DOI: 10.3788/LOP202158.0210018 Cite this Article Set citation alerts

Haicheng Qu, Bowen Tang, Guisen Yuan. Improved Super-Resolution Image Reconstruction Algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210018 Copy Citation Text

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Fig. 1. Overall structure of the SRCNN

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Fig. 2. Structure of the deconvolution

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Fig. 3. Improved SRCNN structure

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Fig. 4. Residual network

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Fig. 5. Improved residual network

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Fig. 6. Reconstruction effects of different algorithms. (a) Bicubic; (b) SRCNN; (c) FSRCNN; (d) our algorithm

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Fig. 7. Reconstruction effect of the actual acquired image. (a) Low-resolution images; (b) our algorithm; (c) high-resolution images

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Algorithm	Baby	Bird	Butterfly	Head	Woman
Bicubic	27.28	35.31	247.33	34.75	93.45
SRCNN	21.97	23.78	134.33	30.86	65.77
FSRCNN	22.41	23.60	127.43	30.53	62.93
RD-SRCNN	21.09	17.69	86.44	28.79	49.95

Table 1. MSE of different algorithms

Algorithm	Baby	Bird	Butterfly	Head	Woman
Bicubic	0.90	0.92	0.82	0.80	0.88
SRCNN	0.91	0.94	0.87	0.81	0.91
FSRCNN	0.91	0.95	0.88	0.82	0.91
RD-SRCNN	0.92	0.96	0.92	0.83	0.93

Table 2. SSIM of different algorithms

Algorithm	Baby	Bird	Butterfly	Head	Woman
Bicubic	33.77	32.65	24.19	32.72	28.42
SRCNN	34.71	34.21	26.54	33.23	29.95
FSRCNN	34.72	34.40	27.07	33.28	30.14
RD-SRCNN	34.88	35.65	28.76	33.54	31.15

Table 3. Reconstruction effects of different algorithms unit: dB

Evaluation indicator	Activation function	Baby	Bird	Butterfly	Head	Woman
MSE	ELU	21.31	23.12	123.51	30.29	58.82
MSE	ReLU	22.41	23.60	127.43	30.53	62.93
SSIM	ELU	0.91	0.94	0.89	0.82	0.92
SSIM	ReLU	0.91	0.94	0.88	0.81	0.91
PSNR /dB	ELU	34.84	34.49	27.21	33.32	30.43
PSNR /dB	ReLU	34.62	34.40	27.07	33.28	30.14

Table 4. Comparison of different activation functions in the 5-layer network structure

Evaluation indicator	Activation function	Baby	Bird	Butterfly	Head	Woman
MSE	ELU	21.23	21.18	106.74	29.27	56.26
MSE	ReLU	22.33	21.86	113.67	29.80	56.43
SSIM	ELU	0.92	0.95	0.90	0.83	0.92
SSIM	ReLU	0.91	0.94	0.89	0.82	0.92
PSNR /dB	ELU	34.84	34.92	27.84	33.46	30.62
PSNR /dB	ReLU	34.73	34.86	27.57	33.39	30.61

Table 5. Comparison of different activation functions in the 8-layer network structure

Evaluation indicator	Residual structure	Baby	Bird	Butterfly	Head	Woman
MSE	yes	21.16	18.18	87.05	29.35	50.17
MSE	no	21.31	23.12	123.57	30.29	58.82
SSIM	yes	0.92	0.95	0.92	0.83	0.93
SSIM	no	0.91	0.94	0.89	0.82	0.92
PSNR /dB	yes	34.88	35.53	28.73	33.45	31.13
PSNR /dB	no	34.84	34.49	27.21	33.32	30.43

Table 6. ELU activation function performance in 5-layer network structure

Evaluation indicator	Residual structure	Baby	Bird	Butterfly	Head	Woman
MSE	yes	21.16	18.18	86.44	29.35	49.95
MSE	no	21.53	20.38	105.43	29.55	56.27
SSIM	yes	0.92	0.96	0.92	0.83	0.93
SSIM	no	0.91	0.95	0.90	0.82	0.92
PSNR /dB	yes	34.88	35.65	28.76	33.54	31.15
PSNR /dB	no	34.79	35.03	27.9	33.42	30.62

Table 7. ELU activation function performance in 8-layer network structure

Evaluation indicator	Deconvolution	Baby	Bird	Butterfly	Head	Woman
MSE	yes	21.09	17.69	86.44	28.79	49.95
MSE	no	21.16	18.18	87.05	29.35	50.17
SSIM	yes	0.92	0.96	0.92	0.83	0.93
SSIM	no	0.92	0.95	0.92	0.83	0.93
PSNR /dB	yes	34.88	35.65	28.76	33.54	31.15
PSNR /dB	no	34.88	35.53	28.73	33.45	31.13

Table 8. Performance indicators of deconvolution and non-deconvolution

Method	Times /s	Method	Time /s
Eight layers with no residual	31.638	5- layer network	11.863
Eight layers with residual	30.071	8-layer network	8.152

Table 9. Comparison of training time

Haicheng Qu, Bowen Tang, Guisen Yuan. Improved Super-Resolution Image Reconstruction Algorithm[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210018

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