Holistic-Super-Resolution-Review
September 17, 2025 · View on GitHub
Review of deep-learning based super-resolution method in different fields.
:blush: The wealth of the literature on SR is so rich that we could not give an exhaustic review. We just list the major methods along the timeline.
:heavy_exclamation_mark: Single Image Super-Resolution (SISR)
We categorize the SISR into TWO types, Regression-Based Models and Generative-Based Models, for their purpose.
:boom: Datasets
:one: Set5
:two: Set14
:three: BSD100
:four: Urban100
:five: Manga109
:six: DIV2K
:seven: Flickr1024
:dart: Experimental Results on X4 Task
| Model | Params (K) | Set5 | Set14 | BSD100 | Urban100 | Training Set/s |
|---|---|---|---|---|---|---|
| Bicubic | - | 28.43/0.8109 | 26.00/0.7023 | 25.96/0.6678 | 23.14/0.6574 | - |
| TPSR-D2 | 61 | 29.60/- | 26.88/- | 26.23/- | 24.12/- | DIV2K |
| LUT | 1274 | 29.82/0.8478 | 27.01/0.7355 | 26.53/0.6953 | 24.02/0.6990 | DIV2K |
| SRCNN | 24 | 30.48/0.8628 | 27.50/0.7513 | 26.90/0.7103 | 24.52/0.7226 | T91+ImageNet |
| ESPCN | 25 | 30.52/0.8697 | 27.42/0.7606 | 26.87/0.7216 | 24.39/0.7241 | T91+ImageNet |
| SPLUT | 18000 | 30.52/0.8630 | 27.54/0.7520 | 26.87/0.7090 | 24.46/0.7190 | DIV2K |
| RankSRGAN | - | - | 26.57/- | 25.57/- | - | DIV2K+Flickr2K |
| FSRCNN | 12 | 30.70/0.8657 | 27.59/0.7535 | 26.96/0.7128 | 24.60/0.7258 | T91+General-100 |
| CSCN | - | 30.86/0.8732 | 27.64/0.7578 | 27.03/0.7161 | - | T91 |
| NatSR | 4800 | 30.98/0.8606 | 27.42/0.7329 | 26.44/0.6827 | 25.46/0.7602 | DIV2K |
| ZSSR | 225 | 31.13/0.8796 | 28.01/0.7651 | 27.12/0.7211 | - | - |
| VDSR | 665 | 31.35/0.8838 | 28.01/0.7674 | 27.29/0.7251 | 25.18/0.7524 | BSD+T91 |
| DSRN | 1200 | 31.40/0.8830 | 28.07/0.7700 | 27.25/0.7240 | 25.08/0.7470 | T91 |
| DRCN | 1775 | 31.53/0.8854 | 28.02/0.7670 | 27.23/0.7233 | 25.14/0.7510 | T91 |
| SESR | 115 | 31.54/0.8866 | 28.12/0.7712 | 27.31/0.7277 | 25.31/0.7604 | DIV2K |
| LapSRN | 812 | 31.54/0.8850 | 28.19/0.7720 | 27.32/0.7270 | 25.21/0.7560 | BSD+T91 |
| DRRN | 207 | 31.68/0.8888 | 28.21/0.7720 | 27.38/0.7284 | 25.44/0.7638 | BSD+T91 |
| ENet-PAT | - | 31.74/- | 28.42/- | 27.50/- | 25.66/- | MSCOCO |
| MemNet | 677 | 31.74/0.8893 | 28.26/0.7723 | 27.40/0.7281 | 25.50/0.7630 | BSD+T91 |
| IDN | 678 | 31.82/0.8903 | 28.25/0.7730 | 27.41/0.7297 | 25.41/0.7632 | BSD+T91 |
| SRResNet | 1500 | 31.92/0.8998 | 28.39/0.8166 | 27.52/0.7603 | - | ImageNet |
| NAPS | 125 | 31.93/0.8906 | 28.42/0.7763 | 27.44/0.7307 | 25.66/0.7715 | DIV2K |
| SRMDNF | - | 31.96/0.8930 | 28.35/0.7770 | 27.49/0.7340 | 25.68/0.7730 | BSD+DIV2K+WED |
| SRDenseNet | 5452 | 32.02/0.7819 | 28.50/0.7782 | 27.53/0.7337 | 26.05/0.7819 | ImageNet |
| MSRN | 6300 | 32.07/0.8903 | 28.60/0.7751 | 27.52/0.7273 | 26.04/0.7896 | DIV2K |
| SMSR | 1006 | 32.12/0.8932 | 28.55/0.7808 | 27.55/0.7351 | 26.11/0.7868 | DIV2K |
| DCLS | - | 32.12/0.8890 | 28.54/0.7728 | 27.60/0.7285 | 26.15/0.7809 | DIV2K+Flickr2K |
| EDSR-SLS | 363 | - | 28.49/- | 27.51/- | 25.84/- | DIV2K |
| CARN | 1600 | 32.13/0.8937 | 28.60/0.7806 | 27.58/0.7349 | 26.07/0.7837 | BSD+T91+DIV2K |
| ESRT | 751 | 32.19/0.8947 | 28.69/0.7833 | 27.69/0.7379 | 26.39/0.7962 | DIV2K |
| IMDN | 715 | 32.21/0.8948 | 28.58/0.7811 | 27.56/0.7353 | 26.04/0.7838 | DIV2K |
| SRFeat | 6189 | 32.27/0.8938 | 28.71/0.7835 | 27.64/0.7378 | - | DIV2K |
| LatticeNet | 777 | 32.30/0.8962 | 28.68/0.7830 | 27.62/0.7367 | 26.25/0.7873 | DIV2K |
| RDN-MetaSR | - | 32.38/- | 28.78/- | 27.71/- | 26.55/- | DIV2K |
| SCN | 1200 | 32.39/0.8981 | 28.74/0.7869 | 27.69/0.7415 | 26.50/0.8000 | DIV2K |
| SRFBN | 3500 | 32.46/0.8968 | 28.80/0.7876 | 27.71/0.7420 | 26.64/0.8033 | DIV2K+Flickr2K |
| EDSR | 43000 | 32.46/0.8968 | 28.80/0.7876 | 27.71/0.7420 | 26.64/0.8033 | DIV2K |
| RDN | 21900 | 32.47/0.8990 | 28.81/0.7871 | 27.72/0.7419 | 26.61/0.8028 | DIV2K |
| DBPN | 10000 | 32.47/0.8980 | 28.82/0.7860 | 27.72/0.7400 | 26.38/0.7946 | DIV2K+Flickr2K |
| RNAN | 225 | 32.49/0.8982 | 28.83/0.7878 | 27.72/0.7421 | 26.61/0.8023 | DIV2K |
| RDN-LIIF | - | 32.50/- | 28.80/- | 27.74/- | 26.68/- | DIV2K |
| OISR | 15592 | 32.53/0.8992 | 28.86/0.7878 | 27.75/0.7428 | 26.79/0.8068 | DIV2K |
| ArbRCAN | 16600 | 32.55/- | 28.87/- | 27.76/- | 26.68/- | DIV2K |
| NLSN | - | 32.59/0.9000 | 28.87/0.7891 | 27.78/0.7444 | 26.96/0.8109 | DIV2K |
| RCAN | 16000 | 32.63/0.9002 | 28.87/0.7889 | 27.77/0.7436 | 26.82/0.8087 | DIV2K |
| SAN | 15700 | 32.64/0.9003 | 28.92/0.7888 | 27.78/0.7436 | 26.79/0.8068 | DIV2K |
| HAN | 64199 | 32.64/0.9002 | 28.90/0.7890 | 27.80/0.7442 | 26.85/0.8094 | DIV2K |
| IPT | 115000 | 32.64/- | 29.01/- | 27.82/- | 27.26/- | ImageNet |
| FAD-RCAN | - | 32.65/0.9007 | 28.88/0.7889 | 27.78/0.7437 | 26.86/0.8092 | DIV2K |
| RFANet | 11000 | 32.66/0.9004 | 28.88/0.7894 | 27.79/0.7442 | 26.92/0.8112 | DIV2K |
| CSNLN | 30000 | 32.68/0.9004 | 28.95/0.7888 | 27.80/0.7439 | 27.12/0.8168 | DIV2K |
| CRAN | 19940 | 32.72/0.9012 | 29.01/0.7918 | 27.86/0.7460 | 27.13/0.8167 | DIV2K |
| DRN | 9800 | 32.74/0.9020 | 28.98/0.7920 | 27.83/0.7450 | 27.03/0.8130 | DIV2K+Flickr2K |
| ELAN | 8312 | 32.75/0.9022 | 28.96/0.7914 | 27.83/0.7459 | 27.13/0.8167 | DIV2K |
| EBRN | 7900 | 32.79/0.9032 | 29.01/0.7903 | 27.85/0.7464 | 27.03/0.8114 | DIV2K |
| DFSA | - | 32.79/0.9019 | 29.06/0.7922 | 27.87/0.7458 | 27.17/0.8163 | DIV2K+Flickr2K |
| SwinIR-LTE | - | 32.81/- | 29.06/- | 27.86/- | 27.24/- | DIV2K |
| SwinIR | 11800 | 32.92/0.9044 | 29.09/0.7950 | 27.92/0.7489 | 27.45/0.8254 | DIV2K+Flickr2K |
| OmniSR | 792 | 32.49/0.8988 | 28.78/0.7859 | 27.71/0.7415 | 26.64/0.8018 | DIV2K |
| ASSLN | 677 | 32.29/0.8964 | 28.69/0.7844 | 27.66/0.7384 | 26.27/0.7907 | DIV2K+Flickr2K |
| SPSR | - | 31.04/0.8772 | 27.07/0.8076 | 26.05/0.6818 | 25.23.0.9531 | DIV2K |
| DLGSANet | 761 | 32.54/0.8993 | 28.84/0.7871 | 27.73/0.7415 | 26.66/0.8033 | DIV2K |
| CRAFT | 753 | 32.52/0.8989 | 28.85/0.7872 | 27.72/0.7418 | 26.56/0.7995 | DIV2K |
| SRFormer | 873 | 32.51/0.8988 | 28.82/0.7872 | 27.73/0.7422 | 26.67/0.8032 | DIV2K |
| WGSR | - | 31.51/0.8690 | 26.69/0.7160 | 26.37/0.6840 | 25.61/0.7770 | DIV2K |
| EQSR | - | 32.71/- | 29.12/- | 27.86/- | 27.30/- | DIV2K+Flickr2K |
| HAT | 20800 | 33.04/0.9056 | 29.23/0.7973 | 28.00/0.7517 | 27.97/0.8368 | DIV2K+Flickr2K |
| DAT | 11212 | 33.08/0.9055 | 29.23/0.7973 | 28.00/0.7515 | 27.87/0.8343 | DIV2K+Flickr2K |
| SAFMN | 240 | 32.18/0.8948 | 28.60/0.7813 | 27.58/0.7359 | 25.97/0.7809 | DIV2K+Flickr2K |
| CFAT | 22070 | 33.19/ 0.9068 | 29.30/0.7985 | 28.17/0.7524 | 28.11/0.8380 | DIV2K+Flickr2K |
:heavy_exclamation_mark: Video Super-Resolution (VSR)
We categorize the SISR into TWO types, Regression-Based Models and Generative-Based Models, for their purpose.
:boom: Datasets
:one: REDS
:two: Video-90K
:three: Vid4
:four: UDM10
:five: UDF
:six: SPMC
:dart: Experimental Results on X4 Task
| Model | Params (M) | Vid4 | Video-90K | REDS | Training Set/s |
|---|---|---|---|---|---|
| Bicubic | - | 23.78/0.6347 | 31.32/0.8684 | 23.72/0.7559 | - |
| VESPCN | - | 25.35/0.7557 | -/- | 24.93/0.8107 | CDVL |
| FRVSR | 5.1 | 26.69/0.8220 | -/- | 25.27/0.8256 | Vimeo-90K |
| SPMC | - | 25.88/0.7752 | -/- | -/- | SPMCS |
| DUF | 5.8 | 27.33/0.8319 | 36.37/0.9387 | 28.63/0.8251 | DUF |
| RBPN | 12.2 | 27.12/0.8180 | 37.07/0.9435 | 25.17/0.8187 | Vimeo-90K |
| PFNL | 3.0 | 26.73/0.8029 | 36.14/0.9363 | 29.63/0.8502 | UDM10 |
| VSR_TGA | - | 27.59/0.8419 | -/- | -/- | Vimeo-90K |
| MuCAN | - | -/- | 37.32/0.9465 | 30.88/0.8750 | REDS+Vimeo-90K |
| RSDN | 6.2 | 27.79/0.8474 | 37.05/0.9454 | -/- | Vimeo-90K |
| BasicVSR | 6.3 | 27.24/0.8251 | 37.18/0.9450 | 31.42/0.8909 | REDS+Vimeo-90K |
| IconVSR | 8.7 | 27.39/0.88279 | 37.47/0.9476 | 31.67/0.8948 | REDS+Vimeo-90K |
| DSMC | - | -/- | -/- | 25.73/0.8428 | REDS |
| VSRT | 32.6 | 27.36/0.8258 | 37.71/0.9494 | 25.73/0.8428 | REDS+Vimeo-90K |
| VRT | 35.6 | 27.93/0.8425 | 38.20/0.9530 | 25.73/0.8428 | REDS |
| TOFlow | 1.4 | 25.89/0.7651 | 33.08/0.9054 | 27.98/0.7990 | Vimeo-90K |
| DNSTNet | - | 27.21/0.8220 | 36.86/0.9387 | - | Vimeo-90K |
| SATeCo | - | 27.44/0.8420 | 38.22/0.9532 | 31.62/0.8932 | Vimeo-90K |
| MIAVSR | 6.35 | 28.20/0.8507 | 38.22/0.9532 | 30.46/- | REDS+Vimeo-90K |
| VideoGigaGAN | 369 | 26.78/- | 35.97/- | 32.78/0.9220 | REDS+Vimeo-90K |
| FTVSR++ | 10.8 | 28.80/0.8680 | - | 32.42/0.9070 | REDS+Vimeo-90K |
| MFPI | 7.3 | 28.11/0.8481 | 38.28/0.9534 | 32.81/0.9106 | REDS+Vimeo-90K |
| RVRT | 10.8 | 27.99/0.8462 | 38.15/0.9527 | 32.75/0.9113 | REDS+Vimeo-90K |
:heavy_exclamation_mark: Stereo Super-Resolution (SSR)
We categorize the SSR into TWO types, Regression-Based Models and Generative-Based Models, for their purpose.
:boom: Datasets
:one: Middlebury
:two: Tsukuba
:three: KITTI 2012
:four: KITTI 2015
:five: Flickr1024
:dart: Experimental Results on X4 Task
| Model | Params (M) | KITTI2015 | KITTI2012 | Middlebury | Training Set/s |
|---|---|---|---|---|---|
| Bicubic | - | 23.90/0.7100 | 24.64/0.7334 | 26.39/0.7564 | - |
| StereoSR | 1.06 | 25.12/0.7679 | 25.94/0.7839 | 28.24/0.8133 | Middlebury+KITTI+Tsukuba |
| PASSRnet | 1.35 | 25.34/0.7722 | 26.18/0.7874 | 28.36/0.8153 | Middlebury+Flickr1024 |
| DASSR | 1.1 | 25.35/0.8740 | 26.96/0.8820 | 29.83/0.9090 | Flickr1024 |
| SRRes+SAM | 1.73 | 25.55/0.7825 | 26.35/0.7957 | 28.76/0.8287 | Flickr1024 |
| CPASSRNet | 42.39 | 25.12/0.7693 | 25.31/0.7712 | 28.31/0.8194 | Middlebury |
| iPASSR | 1.42 | 25.61/0.7850 | 26.47/0.7993 | 29.07/0.8363 | Middlebury+Flickr1024 |
| SSRDE-FNet | 2.24 | 25.74/0.7884 | 26.61/0.8028 | 29.29/0.8407 | Flickr1024 |
| IMSSRnet | 6.89 | 25.59/- | 26.44/- | 29.02/- | Middlebury+Flickr1024 |
| CVCNet | 0.99 | 25.55/0.7801 | 26.35/0.7935 | 28.65/0.8231 | Flickr1024 |
| PFT-SSR | - | 25.76/0.7775 | 26.64/0.7913 | 29.58/0.8418 | Flickr1024+Middlebury |
| LSSR | 1.11 | 26.12/0.7997 | 26.93/0.8097 | 29.86/0.8489 | Flickr1024+Middlebury |
| SCGLANet | 25.29 | 26.94/0.8268 | 27.10/0.8204 | 30.23/0.8628 | Flickr1024+Middlebury |
| MSSFNet | 1.82 | 26.07/0.7990 | 26.88/0.8098 | 29.67/0.8498 | Flickr1024+Middlebury |
| Steformer | 1.34 | 25.74/0.7906 | 26.61/0.8037 | 29.29/0.8424 | Flickr1024 |
:heavy_exclamation_mark: Light Field Super-Resolution (LFSR)
We categorize the LFSR into TWO types, Regression-Based Models and Generative-Based Models, for their purpose.
:boom: Datasets
:one: EPFL
:two: HCInew
:three: HCIold
:four: INRIA | Code：pkyv
:five: STFgantry | Code：qjwv
:dart: Experimental Results on X4 Task
| Model | Params (M) | EPFL | HCInew | HCIold | INRIA | STFgantry |
|---|---|---|---|---|---|---|
| Bicubic | - | 25.14/0.8311 | 27.61/0.8507 | 32.42/0.9335 | 26.82/0.8860 | 25.93/0.8431 |
| VDSR | 0.67 | 27.25/0.8782 | 29.31/0.8828 | 34.81/0.9518 | 29.19/0.9208 | 28.51/0.9012 |
| EDSR | 12 | 27.84/0.8858 | 29.60/0.8874 | 35.18/0.9538 | 29.66/0.9259 | 28.70/0.9075 |
| RCAN | 25 | 27.88/0.8863 | 29.63/0.8880 | 35.20/0.9540 | 29.76/0.9273 | 28.90/0.9110 |
| resLF | 6.79 | 27.46/0.8899 | 29.92/0.9011 | 36.12/0.9651 | 29.64/0.9339 | 28.99/0.9214 |
| LFSSR | 1.16 | 28.27/0.9080 | 30.72/0.9124 | 36.70/0.9690 | 30.31/0.9446 | 30.15/0.9385 |
| LF-ATO | 1.36 | 28.25/0.9120 | 30.88/0.9140 | 37.00/0.9700 | 30.71/0.9490 | 30.61/0.9430 |
| MEG-Net | 1.77 | 28.74/0.9160 | 31.10/0.9180 | 37.28/0.9720 | 30.66/0.9490 | 30.77/0.9450 |
| LF-InterNet | 5.23 | 28.67/0.9143 | 30.98/0.9165 | 37.11/0.9715 | 30.64/0.9486 | 30.53/0.9426 |
| LF-IINet | 4.89 | 29.11/0.9200 | 31.36/0.9210 | 37.62/0.9740 | 31.08/0.9520 | 31.21/0.9500 |
| LF-DFnet | 3.99 | 28.77/0.9165 | 31.23/0.9196 | 37.32/0.9718 | 30.83/0.9503 | 31.15/0.9494 |
| DPT | 3.78 | 28.93/0.9167 | 31.19/0.9186 | 37.39/0.9720 | 30.96/0.9502 | 31.14/0.9487 |
| LFT | 1.16 | 29.25/0.9210 | 31.46/0.9220 | 37.63/0.9740 | 31.20/0.9520 | 31.86/0.9550 |
| DistgSSR | 3.58 | 28.98/0.9190 | 31.38/0.9220 | 37.55/0.9730 | 30.99/0.9520 | 31.63/0.9530 |
| EPIT | 1.47 | 29.34/0.9197 | 31.51/0.9231 | 37.68/0.9737 | 31.37/0.9526 | 32.18/0.9571 |
| LFSAV | 1.54 | 29.37/0.9223 | 31.45/0.9217 | 37.50/0.9721 | 31.27/0.9531 | 31.36/0.9505 |