Fig 1.
Framework of the proposed method, in which SR images produced by the SR network are imported to IQA network, and IQA network outputs the image quality scores in return.
Fig 2.
Architecture of the IQA net, where the feature extraction is realized by cascading convolutional layers in five levels.
Fig 3.
Architecture of the SR net, the network consists of 16 residual blocks and 64 filters, which ensure that multi-level features can be shared to boost the performance.
Fig 4.
The impact of IQA network’s status on the training of the SR network in vision.
(a) is the result of 0812.png in DIV2K dataset using the 1st set of hyper-parameters, (b) is the result of 0812.png in DIV2K dataset using the 2nd set of hyper-parameters, (c) is the result of 0812.png in DIV2K dataset using the 3rd set of hyper-parameters.
Table 1.
Effect of hyper-parameter on performance indicators.
Fig 5.
Comparisons of the performance curve of PI versus the RMSE in different weight coefficients of loss components.
Table 2.
Effect on method performance of different margin.
Table 3.
Performance indicators of different methods on SR dataset with scale factor ×4.
Fig 6.
Scatter graph of RMSE versus PI for our methods and others (Bicubic, EnhanceNet, EDSR, RCAN, EDSR-GAN, EDSR-VGG2,2), where a low PI value indicates better perceived quality and a small RMSE value indicates better absolute quality.
Fig 7.
Visual effects and quantitative comparison (RMSE and Perceptual Index) of our methods with Bicubic, EnhanceNet, RCAN, EDSR-GAN and EDSR-VGG 2,2.
(a) shows the results of 095.png from Urban100 dataset, (b) shows the results of 039.png from BSD100 dataset, (c) shows the results of 001.png from Set5 dataset, (d) shows the results of 011.png from Set14 dataset, (e) shows the results of 804.png from DIV2K dataset, (f) shows the results of 805.png from DIV2K dataset, (g) shows the results of 057.png from Urban100 dataset, (h) shows the results of 080.png from Urban100 dataset, (i) shows the results of 801.png from DIV2K dataset.
Fig 8.
Scatter graph of the number of parameters versus SR performance for our methods and others (Bicubic, EnhanceNet, EDSR, RCAN, EDSR-GAN, EDSR-VGG2,2).
Results are evaluated on Set5 with scale factor ×4: (a) is the number of parameters versus RMSE, (b) is the number of parameters versus PI.