# RETRACTED: Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks
## 4 Experimental Results and Analysis
### Experimental Results and Comparative Analysis  
In order to verify the effectiveness of the proposed image super-resolution algorithm based on DCCNN, the present paper used a trained model to reconstruct the \(LR\) image at \"2\(\times\)\", \"3\(\times\)\", and \"4\(\times\)\" [44] the super resolution. The performance of the proposed DCCNN method was evaluated on the Set5 dataset and Set14 dataset, and the results were compared with the results of the existing bicubic interpolation, A+ [11], SRCNN [19], and EEDS [20] algorithms. Because of the different experimental environments of each algorithm, the contrast images could differ from the original ones. However, the overall trend of the comparison results would not be affected. In order to ensure the rationality and objectivity of the experimental results, two representative datasets were selected to test and contrast the images with rich texture details. The testing results are presented in Figures 5-7, which compare the results of the bicubic interpolation, A+SRCNN, and EEDS methods for different reconstruction times of the butterfly image, zebra image, and comicimage, and select the whole panorama and more obvious parts of the wing texture of the butterfly, the head markings of the zebra, and the cheek and shoulder of the comic. A subjective visual evaluation was carried out. Figure 5: Super-resolution reconstruction results of the image “Butterfly” with “\(\times\)4” scale factor. (**a**) Bicubic [5]; (**b**) A+ [11]; (**c**) SRCNN [18]; (**d**) EEDS [20]; (**e**) The Proposed Method’s; (**f**) Original Image. Figure 6: Super-resolution reconstruction results of the image “Zebra” with “\(\times\)3” scale factor. (**a**) Bicubic [5]; (**b**) A+ [11]; (**c**) SRCNN [18]; (**d**) EEDS [20]; (**e**) The Proposed Method; (**f**) Original Image. Figure 5a-d present four super-resolution images of four contrast models from left to right. Figure 5e is the result of reconfiguration. Figure 5f concentrates on the Set5 testing of the original HR image. The butterfly wing edge of the image produced by the proposed method is sharper relative to the other methods: both the edge and the image are more complete, and the texture is also clearer. In Figures 6 and 7, from left to right, the reconfiguration of the four contrast models is also three times that of the super-resolution effect diagram. Figure 6e presents the reconstruction result of the proposed method, while Figure 6f is the original HR image of the Set14 testing dataset. It was found that the reconstruction effect of the zebra image was more prominent, the reconstruction of the cheek edge from the comic image was sharper, the edge preservation was better, and the details of the shoulder texture were more abundant. The average PSNR and SSIM objective testing indexes under various experimental conditions are presented in Table 1. The best experimental results in the table are marked in bold. As can be seen from the testing results presented in Table 2 below, the results of the proposed algorithm were better than those of the improved algorithm in terms of average PSNR and SSIM, thereby proving the effectiveness of the proposed algorithm.