# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
## 4 Experimental and Analysis
### Comparison with State-of-the-Art Methods
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Scale** & \multicolumn{2}{c}{**Set5**} & \multicolumn{2}{c}{**Set14**} & \multicolumn{2}{c}{**B100**} & \multicolumn{2}{c}{**Urban100**} \\  & **Factor** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline SRCNN & & 36.66 & 0.9542 & 32.45 & 0.9067 & 31.36 & 0.8879 & 29.50 & 0.8946 \\ VDSR & & 37.53 & 0.9587 & 33.03 & 0.9124 & 31.90 & 0.8960 & 30.76 & 0.9140 \\ LapSRN & & 37.52 & 0.959 & 33.08 & 0.913 & 31.80 & 0.8950 & 30.41 & 0.9100 \\ DRCN & & 37.63 & 0.9588 & 33.04 & 0.9118 & 31.85 & 0.8942 & 30.75 & 0.9133 \\ IDN & & 37.83 & 0.96 & 33.30 & 0.9148 & 32.08 & 0.8985 & 31.27 & 0.9196 \\ DPSR & & 37.77 & 0.9591 & 33.48 & 0.9164 & 32.12 & 0.8984 & 31.87 & 0.9256 \\ IMDN & & 38.00 & 0.9605 & 33.63 & 0.9177 & 32.19 & 0.8996 & 32.17 & 0.9283 \\ PAN & & 38.00 & 0.9605 & 33.59 & 0.9181 & 32.18 & 0.8997 & 32.01 & 0.9273 \\ LAPAR-A & & 38.01 & 0.9605 & 33.62 & 0.9183 & 32.19 & 0.8999 & 32.10 & 0.9283 \\ SMSR & & 38.00 & 0.9601 & 33.64 & 0.9179 & 32.17 & 0.8990 & 32.19 & 0.9284 \\ DASR & & 37.87 & 0.9599 & 33.34 & 0.9160 & 32.03 & 0.8986 & 31.49 & 0.9227 \\ DeFiAN & & 38.03 & 0.9605 & 33.62 & 0.9181 & 32.20 & 0.8999 & 32.20 & 0.9286 \\ GFSR & & 38.08 & 0.9612 & 33.74 & 0.9193 & 32.20 & 0.9002 & 32.37 & 0.9302 \\ GFSR+ & & **38.12** & **0.9614** & **33.84** & **0.9203** & **32.24** & **0.9007** & **32.55** & **0.9317** \\ \hline SRCNN & & 32.75 & 0.9090 & 29.30 & 0.8215 & 28.41 & 0.7863 & 26.24 & 0.7989 \\ VDSR & & 33.66 & 0.9213 & 29.77 & 0.8314 & 28.82 & 0.7976 & 27.14 & 0.8279 \\ DRCN & & 33.82 & 0.9226 & 29.76 & 0.8311 & 28.80 & 0.7963 & 27.15 & 0.8276 \\ IDN & & 34.11 & 0.9253 & 29.99 & 0.8354 & 28.95 & 0.8013 & 27.42 & 0.8359 \\ DPSR & & 34.32 & 0.9259 & 30.25 & 0.8410 & 29.08 & 0.8044 & 28.07 & 0.8504 \\ IMDN & & 34.36 & 0.9270 & 30.32 & 0.8417 & 29.09 & 0.8046 & 28.17 & 0.8519 \\ PAN & 3 & 34.40 & 0.9271 & 30.36 & 0.8423 & 29.11 & 0.8050 & 28.11 & 0.8511 \\ LAPAR-A & & 34.36 & 0.9267 & 30.34 & 0.8421 & 29.11 & 0.8054 & 28.15 & 0.8523 \\ SMSR & & 34.40 & 0.9270 & 30.33 & 0.8412 & 29.10 & 0.8050 & 28.25 & 0.8536 \\ DASR & & 34.11 & 0.9254 & 30.13 & 0.8408 & 28.96 & 0.8015 & 27.65 & 0.8450 \\ DeFiAN & & 34.42 & 0.9273 & 30.34 & 0.8410 & 29.10 & 0.8053 & 28.20 & 0.8528 \\ GFSR & & 34.49 & 0.9282 & 30.41 & 0.8437 & 29.14 & 0.8066 & 28.40 & 0.8562 \\ GFSR+ & & **34.56** & **0.9287** & **30.47** & **0.8446** & **29.18** & **0.8073** & **28.52** & **0.8579** \\ \hline SRCNN & & 30.48 & 0.8628 & 27.50 & 0.7513 & 26.90 & 0.7101 & 24.52 & 0.7221 \\ VDSR & & 31.35 & 0.8838 & 28.01 & 0.7674 & 27.29 & 0.7251 & 25.18 & 0.7524 \\ LapSRN & & 31.54 & 0.8850 & 28.19 & 0.7720 & 27.32 & 0.7280 & 25.21 & 0.7560 \\ DRCN & & 31.53 & 0.8854 & 28.02 & 0.7670 & 27.23 & 0.7233 & 25.14 & 0.7510 \\ IDN & & 31.82 & 0.8903 & 28.25 & 0.7730 & 27.41 & 0.7297 & 25.41 & 0.7632 \\ DPSR & & 32.19 & 0.8945 & 28.65 & 0.7829 & 27.58 & 0.7354 & 26.15 & 0.7864 \\ IMDN & & 32.21 & 0.8946 & 28.58 & 0.7811 & 27.56 & 0.7353 & 26.04 & 0.7838 \\ PAN & & 32.13 & 0.8948 & 28.61 & 0.7822 & 27.59 & 0.7363 & 26.11 & 0.7854 \\ LAPAR-A & & 32.15 & 0.8944 & 28.61 & 0.7818 & 27.61 & 0.7366 & 26.14 & 0.7871 \\ SMSR & & 32.12 & 0.8932 & 28.55 & 0.7808 & 27.55 & 0.7351 &Figure 8 illustrates the visual comparison of \"Butterfly\" images in the contrast methods in Set5. From Figure 8, we can see that the images reconstructed by SRCNN, VDSR, LapSRN, DRCN and DPSR are relatively blurry. Although IDN, LAPAP-A, SMSR and DeFiAN can recuperate more high-frequency information, the reconstructed images obtained also contain serious artifacts. Our proposed method, IMDN and DASR are able to recover richer detailed information and get better super-resolution results. Nonetheless, compared with IMDN and DASR, our GFSR method shows stronger performance in both contour preservation and detail restoration, which is reflected in a clearer overall contour, and more abundant information on the edges and textures of butterfly wings.