Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (2): 10-16.doi: 10.6040/j.issn.1672-3961.0.2019.318

• Machine Learning & Data Mining • Previous Articles     Next Articles

Method for super-resolution using parallel interlaced sampling

An ZHU1(),Chu XU2   

  1. 1. Software Institute, Nanjing University, Nanjing 210000, Jiangsu, China
    2. Information Construction Management Service Center, Nanjing University Information Construction Management Service Center, Nanjing 210000, Jiangsu, China
  • Received:2019-06-20 Online:2020-04-20 Published:2020-04-16

Abstract:

Various Internet-based images and artificial intelligence applications were more sensitive to the quality of image data. The image quality had been seriously affected due to the limitations of previous acquisition equipment and transmission methods. In order to compensate for the loss of image data quality and enhance the image effect, a parallel interlaced up and down sampling network (PSUDN) was proposed as a better solution to this problem, which using parallel high resolution feature (HR Feature) and low resolution feature (LR Feature) interleaving sample to generated advanced feature maps, and improved the quality of the output high-resolution pictures by building parallel high resolution feature modules and low resolution feature modules. The model constructed by parallel upsampling and downsampling could reconstruct 8 times high resolution pictures and achieved better results.

Key words: DCNN, super resolution, residual network, parallel interlaced, rebuild high quality high resolution images

CLC Number: 

  • TP319

Fig.1

The network of PSUDN"

Fig.2

The unit for parallel interlaced up and down sampling"

Table 1

Quantitative evaluation of algorithm performance [scale factors 2x, 4x and 8x]"

算法 比例 SET5 SET14 BSDS100 URBAN100 MANGA109
PSNR SSIM PSNR SSIM PSNR SSIM PSNR SSIM PSNR SSIM
Bicubic 2 33.650 0.930 30.340 0.870 29.560 0.844 26.880 0.841 30.840 0.935
VDSR 2 37.530 0.958 32.970 0.913 31.900 0.896 30.770 0.914 37.160 0.974
DRCN 2 37.630 0.959 32.980 0.913 31.850 0.894 30.760 0.913 37.570 0.973
LapSRN 2 37.520 0.959 33.080 0.922 31.800 0.895 30.410 0.910 37.270 0.974
EDSR 2 38.110 0.960 33.920 0.919 33.320 0.901 32.930 0.935 39.100 0.977
PSUDN(Ours) 2 38.130 0.961 33.950 0.919 23.370 0.910 32.900 0.928 39.420 0.977
Bicubic 4 28.420 0.810 26.100 0.704 25.960 0.669 23.150 0.659 24.920 0.789
VDSR 4 31.350 0.882 28.030 0.770 27.290 0.726 25.180 0.753 28.820 0.886
DRCN 4 31.530 0.884 28.040 0.770 27.240 0.724 25.140 0.752 28.970 0.886
LapSRN 4 31.540 0.885 28.190 0.772 27.320 0.728 25.210 0.756 29.090 0.890
EDSR 4 32.460 0.897 28.800 0.788 27.710 0.742 26.630 0.803 31.020 0.915
PSUDN(Ours) 4 32.490 0.899 28.810 0.791 27.710 0.742 26.800 0.810 31.510 0.917
Bicubic 8 24.390 0.657 23.190 0.568 23.670 0.547 20.740 0.515 21.470 0.649
VDSR 8 25.720 0.711 24.210 0.609 24.370 0.576 21.540 0.560 22.830 0.707
LapSRN 8 26.140 0.738 24.440 0.623 24.540 0.586 21.810 0.581 23.390 0.735
EDSR 8 26.970 0.775 24.940 0.640 24.800 0.596 22.470 0.620 24.580 0.778
PSUDN(Ours) 8 27.150 0.779 25.010 0.643 24.880 0.601 22.450 0.622 25.030 0.781

Fig.3

Visualization of the result [4x upscaling]"

Table 2

Quantitative comparison of loss function"

数据集 Loss PSNR SSIM Training epochs
DIV2K L1 26.680 0.714 10 000
DIV2K L2 26.200 0.703 10 000
DIV2K Charbinnier 26.830 0.719 10 000
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