# GPSR: Gradient-Prior-Based Network for Image Super-Resolution
## 4 Experiments
### Results with Bicubic (BI) Degradation
#### 4.3.2 Visual Quality  
Figure 9 illustrates the visual comparisons of \(\times\)4 and \(\times\)8 SR tasks, respectively, which offer the reconstructed results obtained using different methods in the same patches, and the corresponding original HR patches are given as references. From Figure 9, we can see that most of the methods used for comparison are weak in reconstructing sharp edge and texture details, and even produce severe geometric distortions and artificial artifacts. Compared with these methods, our GPSR is able to reconstruct a clearer image with sharp edge and texture details. Remarkably, GPSR suppresses the undesired geometric distortions well, and this advantage becomes increasingly important as the scaling factor increases in SR tasks. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{**FLOPs**} & \multirow{2}{*}{**Scale**} & **Set5** & **Set14** & **BSD100** & **Urban100** & **Manga109** \\  & & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline LapSRN [38] & 0.9M & - & & 31.54/0.8850 & 28.19/0.7720 & 27.32/0.7270 & 25.21/0.7560 & 29.09/0.8900 \\ HAN [12] & 16.07M & 944G & & 32.64/0.9002 & 28.90/0.7890 & 27.80/0.7442 & 26.85/0.8094 & 31.42/0.9177 \\ DBPN [34] & 10.4M & 1106G & & 32.47/0.8983 & 28.75/0.7859 & 27.67/0.7396 & 26.38/0.7947 & 30.90/0.9134 \\ RDN [11] & 22.42M & 1309G & & 32.47/0.8990 & 28.81/0.7871 & 27.72/0.7419 & 26.61/0.8028 & 31.00/0.9151 \\ EDSR [10] & 43.1M & 2894G & & 32.46/0.8968 & 28.80/0.7876 & 27.71/0.7420 & 26.64/0.8033 & 31.03/0.9149 \\ SAN [15] & 15.9M & 936G & 4 & 32.64/0.9003 & 28.92/0.7888 & 27.79/0.7436 & 26.79/0.8068 & 31.18/0.9169 \\ PA-EDSR [14] & 45.53M & 3214G & & 32.65/0.9006 & 28.87/0.7891 & 27.76/0.7445 & 27.01/0.8140 & 31.29/0.9194 \\ DRN [40] & 4.8M & 685G & & 32.61/0.8974 & 28.89/0.7876 & 27.74/0.7389 & 26.71/0.8038 & 31.33/0.9150 \\ NLSN [13] & 46.52M & 2955G & & 32.59/0.9000 & 28.87/0.7891 & 27.78/0.7444 & 26.96/0.8109 & 31.27/0.9184 \\ GPSR & 8.18M & 1098G & & 32.74/0.9010 & 28.98/0.7901 & 27.81/0.7441 & 26.96/0.8116 & 31.50/0.9200 \\ GPSR+ & 8.18M & - & 32.82/0.9022 & 29.06/0.7913 & 27.86/0.7451 & 27.14/0.8150 & 31.80/0.9223 \\ \hline LapSRN [38] & 1.3M & - & 26.15/0.7380 & 24.35/0.6200 & 24.54/0.5860 & 21.81/0.5810 & 23.39/0.7350 \\ HAN [12] & 16.22M & 271G & & 27.33/0.7884 & 25.24/0.6510 & 24.98/0.6059 & 22.98/0.6437 & 25.20/0.8011 \\ DBPN [34] & 23.2M & 236G & & 27.21/0.7844 & 25.13/0.6489 & 24.88/0.6010 & 22.72/0.6315 & 25.50/0.7984 \\ RDN [11] & 22.42M & 327G & & 26.91/0.7717 & 24.92/0.6405 & 24.85/0.5993 & 22.52/0.6211 & 24.60/0.7768 \\ EDSR [10] & 45.5M & 1271G & & 27.22/0.7840 & 25.14/0.6476 & 24.88/0.6010 & 22.70/0.6314 & 24.85/0.7906 \\ SAN [15] & 16M & 269G & 8 & 27.22/0.7840 & 25.14/0.6476 & 24.88/0.6010 & 27.70/0.6314 & 24.85/0.7906 \\ PA-EDSR [14] & 45.53M & 872G & & 27.05/0.7796 & 25.05/0.6456 & 24.93/0.6034 & 22.85/0.6393 & 24.97/0.7915 \\ DRN [40] & 5.4M & 171G & & 27.27/0.7873 & 25.21/0.6500 & 24.96/0.6040 & 22.93/0.6385 & 25.21/0.8016 \\ NLSN [13] & 46.52M & 738G & & 26.57/0.7586 & 24.60/0.6299 & 24.66/0.5920 & 22.14/0.6011 & 24.00/0.7546 \\ GPSR & 14.42M & 382G & & 27.45/0.7935 & 25.29/0.6536 & 25.00/0.6059 & 23.08/0.6474 & 25.40/0.8078 \\ GPSR+ & 14.42M & - & 27.52/0.7956 & 25.38/0.6555 & 25.05/0.6070 & 23.23/0.6518 & 25.64/0.8116 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative metrics obtained by various models for SR task with BI degradation model. The optimal and suboptimal metrics are highlighted in red and blue. Figure 8: Comparisons of the PSNR metric and the number of parameters among different SR models on the Set5 dataset. In Figure 9a, we show visual comparisons of the \(\times\)4 SR task. For the image \"barbara\" from the Set14 dataset, we observe that most of the compared methods, such as LapSRN, EDSR, RDN, and DRN fail to recover the edge details of the book effectively, making their reconstructed images appear blurred and smooth.