Super-resolution reconstruction for colorectal endoscopic images based on a residual network

Yue-kun ZHENG; Ming-feng GE; Zhi-min CHANG; Wen-fei DONG

doi:10.37188/CO.2022-0247

Chinese Optics, Volume. 16, Issue 5, 1022(2023)

Super-resolution reconstruction for colorectal endoscopic images based on a residual network

Yue-kun ZHENG^1,2, Ming-feng GE^2、*, Zhi-min CHANG², and Wen-fei DONG^1,2

¹School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China

²Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, China

show less

Abstract Get PDF(in Chinese)

Figures & Tables(15)

Fig. 1. Architecture of SMRAN

Download full size

View in Article

Fig. 2. Res-Sobel Block

Download full size

View in Article

Fig. 3. Multi-scale feature extraction block(MEB)

Download full size

View in Article

Fig. 4. CBAM attention mechanism

Download full size

View in Article

Fig. 5. SMRAB Structure

Download full size

View in Article

Fig. 6. PSNR curve of validation set

Download full size

View in Article

Fig. 7. SSIM curve of validation set

Download full size

View in Article

Fig. 8. Comparison of reconstruction effects of endoscopic images of colorectal polyps using different super-resolution algorithms

Download full size

View in Article

Fig. 9. Improvement of optical resolution by SMRAN model

Download full size

View in Article

Table 1. PSNR values of different algorithms on the testing set (Unit: dB)
View table
View in Article
Table 1. PSNR values of different algorithms on the testing set (Unit: dB)
算法 PSNR(dB)
×2 ×3 ×4
Bicubic 33.85 31.94 29.91
SRCNN 36.70 34.53 32.04
FSRCNN 37.63 35.22 32.23
EDSR 37.34 35.25 32.13
ESPCN 36.75 34.78 31.38
RCAN 39.04 35.63 33.86
本文算法 39.69 36.92 34.25

Table 2. SSIM values of different algorithms on the testing set
View table
View in Article
Table 2. SSIM values of different algorithms on the testing set
算法 SSIM
×2 ×3 ×4
Bicubic 0.9121 0.8824 0.8103
SRCNN 0.9400 0.8983 0.8642
FSRCNN 0.9382 0.9132 0.8660
EDSR 0.9325 0.9158 0.8401
ESPCN 0.9392 0.9003 0.8566
RCAN 0.9483 0.9182 0.8667
本文算法 0.9559 0.9249 0.8675

Table 3. PSNR and SSIM values for different loss functions
View table
View in Article
Table 3. PSNR and SSIM values for different loss functions
损失函数 PSNR(dB) SSIM
${L_1}$ 34.27 0.8664
${L_1}$+MS_SSIM 34.25 0.8675

Table 4. The impact of each module on performance
View table
View in Article
Table 4. The impact of each module on performance
Res-Sobel Block MEB CBAM PSNR(dB)/SSIM
√ √ 33.33/0.8577
√ √ 33.39/0.8601
√ √ 33.47/0.8609
√ √ √ 34.25/0.8675

Table 5. PSNR values of different algorithms on the Kvasir-SEG dataset (Unit: dB)
View table
View in Article
Table 5. PSNR values of different algorithms on the Kvasir-SEG dataset (Unit: dB)
算法 PSNR(dB)
×2 ×3 ×4
Bicubic 37.35 33.86 31.78
SRCNN 39.98 35.98 33.23
FSRCNN 40.62 36.57 33.68
EDSR 40.94 36.69 33.99
ESPCN 39.71 35.91 33.01
RCAN 41.58 37.62 34.23
本文算法 41.80 37.81 34.56

Table 6. SSIM values of different algorithms on the Kvasir-SEG dataset
View table
View in Article
Table 6. SSIM values of different algorithms on the Kvasir-SEG dataset
算法 SSIM
×2 ×3 ×4
Bicubic 0.9776 0.9469 0.9079
SRCNN 0.9833 0.9658 0.9242
FSRCNN 0.9859 0.9672 0.9256
EDSR 0.9867 0.9669 0.9219
ESPCN 0.9886 0.9710 0.9396
RCAN 0.9859 0.9702 0.9447
本文算法 0.9884 0.9714 0.9456

Tools

Get Citation

Copy Citation Text

Yue-kun ZHENG, Ming-feng GE, Zhi-min CHANG, Wen-fei DONG. Super-resolution reconstruction for colorectal endoscopic images based on a residual network[J]. Chinese Optics, 2023, 16(5): 1022

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites