Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution
| Model | Year | #Param | Set5 | Set14 | B100 | Urban100 | Manga109 |
|---|---|---|---|---|---|---|---|
| IMDN | ACM MM'19 | 715K | 32.21/0.8948 | 28.58/0.7811 | 27.56/0.7353 | 26.04/0.7838 | 30.45/0.9075 |
| LAPAR-A | NeurIPS'20 | 659K | 32.15/0.8944 | 28.61/0.7818 | 27.61/0.7366 | 26.14/0.7871 | 30.42/0.9074 |
| SMSR | CVPR'21 | 1,006K | 32.12/0.8932 | 28.55/0.7808 | 27.55/0.7351 | 26.11/0.7868 | 30.54/0.9085 |
| ECBSR | ACM MM'21 | 603K | 31.92/0.8946 | 28.34/0.7817 | 27.48/0.7393 | 25.81/0.7773 | 30.15/0.8315 |
| FDIWN | AAAI'22 | 664K | 32.23/0.8955 | 28.66/0.7829 | 27.62/0.7380 | 26.28/0.7919 | 30.63/0.9098 |
| ShuffleMixer | NeurIPS'22 | 411K | 32.21/0.8953 | 28.66/0.7827 | 27.61/0.7366 | 26.08/0.7835 | 30.65/0.9093 |
| SwinIR-light | ICCV'21 | 844K | 32.44/0.8976 | 28.77/0.7858 | 27.69/0.7406 | 26.47/0.7980 | 30.92/0.9151 |
| TCSR-B | 2023 | 682K | 32.43/0.8977 | 28.84/0.7871 | 27.72/0.7412 | 26.51/0.7994 | 31.01/0.9153 |
| TCSR-L | 2023 | 1,030K | 32.55/0.8992 | 28.89/0.7886 | 27.75/0.7423 | 26.67/0.8039 | 31.17/0.9170 |