# Frequency-Separated Attention Network for Image Super-Resolution
## 4 Experiments
### Comparison with State-of-the-Art Methods
#### 4.3.2 Analysis of Model Comparisons
Equally important  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Scale**} & \multicolumn{2}{c}{**Set5**} & **Set14** & **BSD100** & **Urban100** & **Manga109** \\ \cline{3-6}  & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline Bicubic & \(\times 2\) & 33.68/0.9304 & 30.24/0.8691 & 29.56/0.8453 & 26.88/0.8405 & 30.80/0.9399 \\ SelfExSR & \(\times 2\) & 36.50/0.9537 & 32.23/0.9036 & 31.18/0.8855 & 29.38/0.9032 & \(-/-\) \\ Laplacian & \(\times 2\) & 25.91/0.8200 & 24.31/0.7825 & 24.19/0.7653 & 22.10/0.7643 & 24.19/0.8422 \\ SRCNN & \(\times 2\) & 36.66/0.9542 & 32.45/0.9067 & 31.36/0.8879 & 29.51/0.8946 & 35.60/0.9663 \\ FSRCNN & \(\times 2\) & 36.98/0.9556 & 32.62/0.9087 & 31.50/0.8904 & 29.51/0.8946 & 35.67/0.9710 \\ VDSR & \(\times 2\) & 37.53/0.9587 & 33.05/0.9127 & 31.90/0.8904 & 30.77/0.9141 & 37.22/0.9750 \\ HDRN & \(\times 2\) & 37.75/0.9590 & 33.49/0.9150 & 32.03/0.8980 & 31.87/0.9250 & 38.07/0.9770 \\ CARN & \(\times 2\) & 37.76/0.9590 & 33.52/0.9166 & 32.09/0.8978 & 31.92/0.9256 & 38.36/0.9764 \\ MemNet & \(\times 2\) & 37.78/0.9597 & 33.28/0.9142 & 32.08/0.8978 & 31.31/0.9195 & 37.72/0.9740 \\ SRMD & \(\times 2\) & 37.79/0.9601 & 33.32/0.9159 & 32.05/0.8985 & 31.33/0.9204 & 38.07/0.9761 \\ IMDN & \(\times 2\) & 38.00/0.9605 & 33.63/0.9177 & 32.19/0.8996 & 32.17/0.9283 & 38.88/0.9774 \\ LAPAR-A & \(\times 2\) & 38.01/0.9605 & 33.62/0.9183 & 32.19/0.8999 & 32.10/0.9283 & 38.67/0.9772 \\ A2F-L & \(\times 2\) & 38.09/0.9607 & 33.78/0.9192 & 32.23/0.9002 & 32.46/0.9313 & 38.95/0.9772 \\ FENet & \(\times 2\) & 38.08/0.9608 & 33.70/0.9184 & 32.20/0.9001 & 32.18/0.9287 & 38.89/0.9775 \\ Ours & \(\times 2\) & **38.20/0.9612** & **33.88/0.9200** & **32.33/0.9016** & **32.83/0.9351** & **39.11/0.9781** \\ \hline Bicubic & \(\times 3\) & 30.40/0.8686 & 27.54/0.7741 & 27.21/0.7389 & 24.46/0.7349 & 26.95/0.8556 \\ SelfExSR & \(\times 3\) & 32.62/0.9094 & 29.16/0.8197 & 28.30/0.7843 & \(-/-\) & \(-/-\) \\ Laplacian & \(\times 3\) & 25.29/0.7246 & 24.03/0.6718 & 24.02/0.6496 & 21.77/0.6485 & 23.77/0.7616 \\ SRCNN & \(\times 3\) & 32.75/0.9090 & 29.29/0.8215 & 28.41/0.7863 & 26.24/0.7991 & 30.48/0.9117 \\ FSRCNN & \(\times 3\) & 33.16/0.9140 & 29.42/0.8242 & 28.52/0.7893 & 26.41/0.8064 & 31.10/0.9210 \\ VDSR & \(\times 3\) & 33.66/0.9213 & 29.78/0.8318 & 28.83/0.9796 & 27.14/0.8279 & 32.01/0.9340 \\ HDRN & \(\times 3\) & 34.24/0.9240 & 30.23/0.8400 & 28.96/0.8040 & 27.93/0.8490 & 33.17/0.9420 \\ CARN & \(\times 3\) & 34.29/0.9255 & 30.29/0.8407 & 29.06/0.8034 & 28.06/0.8493 & 33.49/0.9440 \\ MemNet & \(\times 3\) & 34.09/0.9248 & 30.00/0.8350 & 28.96/0.8001 & 27.56/0.8376 & 32.51/0.9369 \\ SRMD & \(\times 3\) & 34.12/0.9254 & 30.04/0.8382 & 28.97/0.8025 & 27.57/0.8398 & 33.00/0.9403 \\ IMDN & \(\times 3\) & 34.36/0.9270 & 30.32/0.8417 & 29.09/0.8046 & 28.17/0.8519 & 33.61/0.9445 \\ LAPAR-A & \(\times 3\) & 34.36/0.9267 & 30.34/0.8421 & 29.11/0.8054 & 28.15/0.8523 & 33.51/0.9441 \\ A2F-L & \(\times 3\) & 34.54/0.9283 & 30.41/0.8436 & 29.14/0.8062 & 28.40/0.8574 & 33.83/0.9463 \\ FENet & \(\times 3\) & 34.40/0.9273 & 30.36/0.8422 & 29.12/0.8060 & 28.17/0.8524 & 33.52/0.9444 \\ Ours & \(\times 3\) & **34.64/0.9299** & **30.51/0.8456** & **29.21/0.8078** & **28.70/0.8633** & **34.04/0.9474** \\ \hline Bicubic & \(\times 4\) & 28.43/0.8109 & 26.00/0.7023 & 25.96/0.6678 & 23.14/0.6574 & 24.89/0.7866 \\ SelfExSR & \(\times 4\) & 30.33/0.8623 & 27.40/0.7518 & 26.85/0.7108 & 24.82/0.7386 & \(-/-\) \\ Laplacian & \(\times 4\) & 27.22/0.7544 & 25.41/0.6772 & 25.46/0.6492 & 22.71/0.6358 & 24.44/0.7567 \\ SRCNN & \(\times 4\) & 30.48/0.8628 & 27.50/0.7513 & 26.90/0.7103 & 24.52/0.7226 & 27.58/0.8555 \\ FSRCNN & \(\times 4\) & 30.70/0.8657 & 27.59/0.7535 & 26.96/0.7128 & 24.60/0.7258 & 27.90/0.8610 \\ VDSR & \(\times 4\) & 31.25/0.8838 & 28.02/0.7678 & 27.29/0.7252 & 25.18/0 is the computational complexity. Our proposed FSANet for a scale factor of 2 has a computational burden of 150G Flops.