## 4 Experimental Results and Discussion
### Stereoscopic Image SR Results  
#### 4.3.1 Contrast with Single-View Methods  
To demonstrate that the proposed method is more effective than single-view methods, comparative experiments were carried out between the SC method [13], SRCNN method [15], and proposed method. In addition to the common PSNR index, we used two other objective evaluation indices, namely the structural similarity index (SSIM) [35] and the blind/referenceless image spatial quality evaluator (BRISQUE) [36]. Since the PSNR and SSIM indices are full-reference image quality assessments and the BRISQUE index is a no-reference image quality assessment, we considered PSNR and SSIM together for facilitating the analysis. Figure 8 depicts the reconstruction results of these three methods for the first frame of the Pozan Street sequence. It is seen that, according to the details from the local amplification region, SC [13] produced a relatively vague reconstruction. Furthermore, as shown by the edge of the letter P and the number 0, the proposed method was more effective in ringing reduction compared with SRCNN [15]. Figure 9 shows the reconstruction results of the three methods for the Umbrella image. From the magnified details of the umbrella stick and umbrella skeleton, the proposed method produced sharper edges that more closely approximate the real HR images. Figure 10 additionally shows that the details of the pantomismic clothes in the first frame of the Pantomime sequence obtained by the proposed method are clearer. Table 1 presents the PSNR and SSIM values in the Y channel obtained by SC [13], SRCNN [15], and the proposed method. As shown  
Figure 7: Convergence curve. (**a**) Training performance on Middleburry Stereo Dataset [30]; (**b**) Testing the performance on the image Dog.  
in the table, compared with SC [13] and SRCNN [15], the average PSNR value of the proposed method is increased by 2.39 dB and 0.54 dB, respectively, and the average SSIM value of the proposed method is slightly increased by 0.01 and 0.0002, respectively. Furthermore, the BRISQUE value in Table 2, which typically has a value between 0 and 100 (0 represents the best quality, 100 the worst), demonstrates the significant performance of the proposed method. The average BRISQUE value of the proposed method is decreased by 21.66 and 2.03 compared with SC [13] and SRCNN [15]. Although the CNN architecture of the proposed method is similar to that of SRCNN [15], the proposed method achieves better performance benefiting from the combination of the high-precision view difference image estimated by the modified CNN and the global reconstruction constraint. Overall, according to the tables and the reconstructed images, the proposed method achieves a better SR result than SC [13] and SRCNN [15].  
Figure 8: Super-resolution (SR) results and Peak Signal to Noise Ratio (PSNR) values in the Y channel for the first frame of the Pozan Street sequence. (**a**) Ground truth; (**b**) local amplification region of the ground truth; (**c**) local amplification region of sparse coding (SC) [13]; (**d**) local amplification region of SRCNN [15]; and (**e**) local amplification region of the proposed method.  
Figure 9: SR results and PSNR values in the Y channel for the Umbrella image. (**a**) Ground truth; (**b**) local amplification region of the ground truth; (**c**) local amplification region of SC [13]; (**d**) local amplification region of SRCNN [15]; and (**e**) local amplification region of the proposed method.