# RSTSRN: Recursive Swin Transformer Super-Resolution Network for Mars Images
## 4 Results
### RSTSRN Results for Natural Images
Unlike other lightweight SR reconstruction networks, the RSTSRN uses a progressive up-sampling framework based on the Laplacian image pyramid and an interstage parameter sharing mechanism. Therefore, the same parameter set is used in 2\(\times\), 4\(\times\) and  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Algorithm** & **Scale** & **Year** & **Parameters** & \multicolumn{2}{c}{**Set5**} & \multicolumn{1}{c}{**Set14**} & \multicolumn{1}{c}{**BSDS100**} & \multicolumn{1}{c}{**Urban100**} & \multicolumn{1}{c}{**MANGA109**} \\  & & & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline Bicubic & 4\(\times\) & - & - & - & 28.43/0.8022 & 26.10/0.6936 & 25.97/0.6517 & 23.14/0.6599 & 24.91/0.7826 \\ VDSR [45] & 4\(\times\) & 2016 & 665K & 31.35/0.8838 & 28.01/0.7674 & 27.29/0.7251 & 25.18/0.7524 & 28.83/0.8809 \\ DRCN [46] & 4\(\times\) & 2016 & 1774K & 31.53/0.8840 & 28.04/0.7700 & 27.24/0.7240 & 25.14/0.7520 & 28.97/0.8860 \\ MemNet [49] & 4\(\times\) & 2017 & 677K & 31.74/0.8893 & 28.26/0.7723 & 27.40/0.7281 & 25.50/0.7630 & 29.42/0.8942 \\ LapSRN [10] & 4\(\times\) & 2017 & 813K & 31.54/0.8856 & 28.19/0.7720 & 27.32/0.7280 & 25.21/0.7560 & 29.09/0.8900 \\ EDSR [72] & 4\(\times\) & 2017 & 43000K & 32.46/0.8968 & 28.80/0.7876 & 27.71/0.7420 & 26.64/0.8033 & 31.02/0.9148 \\ CARN [73] & 4\(\times\) & 2018 & 1592K & 32.13/0.8937 & 28.60/0.7806 & 27.58/0.7349 & 26.07/0.7837 & 30.36/0.9082 \\ D-DBPN [74] & 4\(\times\) & 2018 & 10291K & 32.47/0.8970 & 28.82/0.7860 & 27.72/0.7400 & 26.38/0.7946 & 30.91/0.9137 \\ MSRN [75] & 4\(\times\) & 2018 & 6078K & 32.07/0.8903 & 28.60/0.7751 & 27.52/0.7273 & 26.04/0.7896 & 30.31/0.9034 \\ IMDN [76] & 4\(\times\) & 2019 & 715K & 32.21/0.8948 & 28.58/0.7811 & 27.56/0.7353 & 26.04/0.7838 & 30.45/0.9075 \\ AWSRN [77] & 4\(\times\) & 2019 & 1587K & 32.27/0.8960 & 28.69/0.7843 & 27.64/0.7385 & 26.29/0.7930 & 30.72/0.9109 \\ RFDN-L [78] & 4\(\times\) & 2020 & 643K & 32.28/0.8957 & 28.61/0.7818 & 27.58/0.7363 & 26.20/0.7883 & 30.61/0.9096 \\ LatticeNet [79] & 4\(\times\) & 2020 & 777K & 32.30/0.8962 & 28.68/0.7830 & 27.62/0.7367 & 26.25/0.7873 & - \\ HNCT [80] & 4\(\times\) & 2022 & 372K & 32.31/0.8957 & 28.71/0.7834 & 27.63/0.7381 & 26.20/0.7896 & 30.70/0.9112 \\ ELAN-light [81] & 4\(\times\) & 2022 & 601K & 32.43/0.8975 & 28.78/0.7858 & 27.69/0.7406 & 26.54/0.7982 & 30.92/0.9150 \\ ESRT [82] & 4\(\times\) & 2022 & 751K & 32.19/0.8947 & 28.69/0.7838 & 27.69/0.7379 & 26.39/0.7962 & 30.75/0.9100 \\ SPIN [83] & 4\(\times\) & 2023 & 555K & 32.48/0.8983 & 28.80/0.7862 & 27.70/0.7415 & 26.55/0.7998 & 30.98/0.9156 \\ DiVANet [84] & 4\(\times\) & 2023 & 939K & 32.41/0.8973 & 28.70/0.7844 & 27.65/0.7391 & 26.42/0.7958 & 30.73/0.9119 \\ NGSwin [55] & 4\(\times\) & 2023 & 1019K & 32.33/0.8963 & 28.78/0.7859 & 27.66/0.7396 & 26.45/0.7963 & 30.80/0.9128 \\ RSTSRN (ours) & 4\(\times\) & - & 1830K & 32.63/0.9008 & 28.89/0.7895 & 27.77/0.7435 & 26.78/0.8075 & 31.40/0.9192 \\ \hline Bicubic & 8\(\times\) & - & - & 24.40/0.6045 & 23.19/0.5110 & 23.67/0.4808 & 20.74/0.4841 & 21.46/0.6138 \\ VDSR [45] & 8\(\times\) & 2016 & 665K & 25.73/0.6743 & 23.20/0.5110 & 24.34/0.5169 & 21.48/0.5289 & 22.73/0.6688 \\ DRCN [46] & 8\(\times\) & 2016 & 1774K & 25.93/0.6743 & 24.25/0.5510 & 24.49/0.5168 & 21.71/0.5289 & 23.20/0.6686 \\ MemNet [49] & - & 2017 & - & - & - & - & - & - \\ LapSRN [10] & 8\(\times\) & 2017 & 813K & 26.15/0.7028 & 24.45/0.5792 & 24.54/0.5293 & 21.81/0.5555 & 23.39/0.7068 \\ EDSR [72] & 8\(\times\) & 2017 & 43000K & 26.96/0.7762 & 24.91/0.6420 & 24.81/0.5985 & 22.51/0.6221 & 24.69/0.7841 \\ CARN [73] & - & 2018 & - & - & - & - & - & - \\ D-DBPN [74] & 8\(\times\) & 2018 & 23071K & 27.21/0.7840 & 25.13/0.6480 & 24.88/0.6010 & 22.73/0.6312 & 25.14/0.7987 \\ MSRN [75] & 8\(\times\) & 2018 & 6226K & 26.59/0.7254 & 24.88/0.5961 & 24.70/0.5410 & 22.37/0.5977 & 24.28/0.7517 \\ IMDN [76] & - & 2019 & -8\(\times\) SR tasks, which makes the RSTSRN demonstrate higher lightweight performance in multi-scale reconstruction tasks.