# RSTSRN: Recursive Swin Transformer Super-Resolution Network for Mars Images
## 4 Results  
### RSTSRN Results for Natural Images  
We compare the proposed RSTSRN with 18 SR algorithms: VDSR [45], DRCN [46], MemNet [49], LapSRN [10], EDSR (Enhanced Deep Super-Resolution) [72], CARN (CAScading Residual Network) [73], D-DBPNs (Dense Deep Back-Projection Networks) [74], MSRN (Multi-Scale Residual Network) [75], IMDN (Information Multi-Distillation Network) [76], AWSRN (Adaptive Weighted Super-Resolution Network) [77], RFDN (Residual Feature Distillation Network) [78], LatticeNet [79], HNCT (Hybrid Network of CNN and Transformer) [80], ELAN (Efficient Long-range Attention Network) [81], ESRT (Efficient Super-Resolution Transformer) [82], SPIN (Super Token Interaction Network) [83], DiVANet (Directional Variance Attention Network) [84] and NGswin [55]. We evaluate the SR images with Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) [85]. Table 1 shows quantitative comparisons for 2\(\times\), 4\(\times\) and 8\(\times\) SR.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Scale**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Parameters**} & \multicolumn{1}{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** \\ \hline Bicubic & 2\(\times\) & - & - & 33.66/0.9284 & 30.34/0.8675 & 29.57/0.8434 & 26.88/0.8438 & 30.82/0.9332 \\ VDSR [45] & 2\(\times\) & 2016 & 665K & 37.53/0.9587 & 33.03/0.9124 & 31.90/0.8960 & 30.76/0.9140 & 37.22/0.9729 \\ DRCN [46] & 2\(\times\) & 2016 & 1774K & 37.63/0.9588 & 33.04/0.9118 & 31.85/0.8942 & 30.75/0.9133 & 37.63/0.9723 \\ MemNet [49] & 2\(\times\) & 2017 & 677K & 37.78/0.9597 & 33.28/0.9142 & 32.08/0.8978 & 31.31/0.9195 & 37.27/0.9740 \\ LapSRN [10] & 2\(\times\) & 2017 & 813K & 37.52/0.9581 & 33.08/0.9109 & 31.80/0.8949 & 30.41/0.9112 & 37.27/0.9745 \\ EDSR [72] & 2\(\times\) & 2017 & 43000K & 38.11/0.9601 & 33.92/0.9195 & 32.32/0.9013 & 32.93/0.9351 & 39.10/0.9773 \\ CARN [73] & 2\(\times\) & 2018 & 1592K & 37.76/0.9590 & 33.52/0.9166 & 32.09/0.8978 & 31.51/0.9312 & 38.36/0.9764 \\ D-DBPN [74] & 2\(\times\) & 2018 & 5819K & 38.09/0.9600 & 33.85/0.9190 & 32.27/0.9000 & 32.55/0.9324 & 38.89/0.9775 \\ MSRN [75] & 2\(\times\) & 2018 & 5930K & 38.08/0.9605 & 33.74/0.9170 & 32.23/0.9013 & 32.22/0.9326 & 38.69/0.9772 \\ IMDN [76] & 2\(\times\) & 2019 & 694K & 38.00/0.9605 & 33.63/0.9177 & 32.19/0.8996 & 32.17/0.9283 & 38.88/0.9774 \\ AWSRN [77] & 2\(\times\) & 2019 & 1397K & 38.11/0.9608 & 33.78/0.9189 & 32.26/0.9006 & 32.49/0.9316 & 38.87/0.9776 \\ RFDN-L [78] & 2\(\times\) & 2020 & 626K & 38.08/0.9606 & 33.67/0.9190 & 32.18/0.8996 & 32.24/0.9290 & 38.95/0.9773 \\ LatticeNet [79] & 2\(\times\) & 2020 & 756K & 38.15/0.9610 & 33.78/0.9193 & 32.25/0.9005 & 32.43/0.930 As shown in Table 1, the proposed RSTSRN achieves superior performance (optimal value or near-optimal values) on 2\(\times\), 4\(\times\) and 8\(\times\) SR tasks to state-of-the-art methods with similar parameters. The RSTSRN obtains the highest PSNR and SSIM values in all five datasets tested at the 4\(\times\) and 8\(\times\)SR tasks, and it has a large lead over other algorithms, indicating that our RSTSRN has great performance superiority in the current lightweight methods. We fully verified the effectiveness of RSTB for high-frequency feature learning on high magnification super-resolution reconstruction tasks. For SR tasks with different multiples, most algorithms will modify the local details of the network model accordingly, and they mainly focus on 2\(\times\), 3\(\times\) and 4\(\times\) SR tasks. There are relatively few algorithms that focus on the 8 \(\times\) SR task. Table 2 shows quantitative comparisons with the number of parameters of lightweight SR algorithms on multi-scale tasks.