超分辨率模型¶
BasicVSR (CVPR’2021)¶
BasicVSR (CVPR'2021)
@InProceedings{chan2021basicvsr,
author = {Chan, Kelvin CK and Wang, Xintao and Yu, Ke and Dong, Chao and Loy, Chen Change},
title = {BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond},
booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},
year = {2021}
}
对于 REDS4,我们对 RGB 通道进行评估。对于其他数据集,我们对 Y 通道进行评估。我们使用 PSNR
和 SSIM
作为指标。
SPyNet 的 预训练权重在这里。
算法 | REDS4 (BIx4) PSNR/SSIM (RGB) |
Vimeo-90K-T (BIx4) PSNR/SSIM (Y) |
Vid4 (BIx4) PSNR/SSIM (Y) |
UDM10 (BDx4) PSNR/SSIM (Y) |
Vimeo-90K-T (BDx4) PSNR/SSIM (Y) |
Vid4 (BDx4) PSNR/SSIM (Y) |
下载 |
---|---|---|---|---|---|---|---|
basicvsr_reds4 | 31.4170/0.8909 | 36.2848/0.9395 | 27.2694/0.8318 | 33.4478/0.9306 | 34.4700/0.9286 | 24.4541/0.7455 | 模型 | 日志 |
basicvsr_vimeo90k_bi | 30.3128/0.8660 | 37.2026/0.9451 | 27.2755/0.8248 | 34.5554/0.9434 | 34.8097/0.9316 | 25.0517/0.7636 | 模型 | 日志 |
basicvsr_vimeo90k_bd | 29.0376/0.8481 | 34.6427/0.9335 | 26.2708/0.8022 | 39.9953/0.9695 | 37.5501/0.9499 | 27.9791/0.8556 | 模型 | 日志 |
BasicVSR++ (CVPR’2022)¶
BasicVSR++ (CVPR'2022)
@InProceedings{chan2022basicvsrplusplus,
author = {Chan, Kelvin C.K. and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},
title = {BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment},
booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},
year = {2022}
}
</details>
SPyNet 的 预训练权重在[这里](https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth)。
<table border="1" class="docutils">
<thead>
<tr>
<th style="text-align: center;">算法</th>
<th style="text-align: center;">REDS4 (BIx4) PSNR/SSIM (RGB)</th>
<th style="text-align: center;">Vimeo-90K-T (BIx4) PSNR/SSIM (Y)</th>
<th style="text-align: center;">Vid4 (BIx4) PSNR/SSIM (Y)</th>
<th style="text-align: center;">UDM10 (BDx4) PSNR/SSIM (Y)</th>
<th style="text-align: center;">Vimeo-90K-T (BDx4) PSNR/SSIM (Y)</th>
<th style="text-align: center;">Vid4 (BDx4) PSNR/SSIM (Y)</th>
<th style="text-align: center;">Download</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: center;"><a href="https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4.py">basicvsr_plusplus_c64n7_8x1_600k_reds4</a></td>
<td style="text-align: center;"><strong>32.3855/0.9069</strong></td>
<td style="text-align: center;">36.4445/0.9411</td>
<td style="text-align: center;">27.7674/0.8444</td>
<td style="text-align: center;">34.6868/0.9417</td>
<td style="text-align: center;">34.0372/0.9244</td>
<td style="text-align: center;">24.6209/0.7540</td>
<td style="text-align: center;"><a href="https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth">model</a> | <a href="https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json">log</a></td>
</tr>
<tr>
<td style="text-align: center;"><a href="https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi.py">basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi</a></td>
<td style="text-align: center;">31.0126/0.8804</td>
<td style="text-align: center;"><strong>37.7864/0.9500</strong></td>
<td style="text-align: center;"><strong>27.7882/0.8401</strong></td>
<td style="text-align: center;">33.1211/0.9270</td>
<td style="text-align: center;">33.8972/0.9195</td>
<td style="text-align: center;">23.6086/0.7033</td>
<td style="text-align: center;"><a href="https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth">model</a> | <a href="https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json">log</a></td>
</tr>
<tr>
<td style="text-align: center;"><a href="https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd.py">basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd</a></td>
<td style="text-align: center;">29.2041/0.8528</td>
<td style="text-align: center;">34.7248/0.9351</td>
<td style="text-align: center;">26.4377/0.8074</td>
<td style="text-align: center;"><strong>40.7216/0.9722</strong></td>
<td style="text-align: center;"><strong>38.2054/0.9550</strong></td>
<td style="text-align: center;"><strong>29.0400/0.8753</strong></td>
<td style="text-align: center;"><a href="https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth">model</a> | <a href="https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json">log</a></td>
</tr>
</tbody>
</table>
<details>
<summary align="left">NTIRE 2021 模型权重文件</summary>
请注意,以下模型是从较小的模型中微调而来的。 这些模型的训练方案将在 MMEditing 达到 5k star 时发布。 我们在这里提供预训练的模型。
[NTIRE 2021 Video Super-Resolution](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_vsr_20210311-1ff35292.pth)
[NTIRE 2021 Quality Enhancement of Compressed Video - Track 1](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track1_20210223-7b2eba02.pth)
[NTIRE 2021 Quality Enhancement of Compressed Video - Track 2](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track2_20210314-eeae05e6.pth)
[NTIRE 2021 Quality Enhancement of Compressed Video - Track 3](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track3_20210304-6daf4a40.pth)
</details>
DIC (CVPR’2020)¶
DIC (CVPR'2020)
@inproceedings{ma2020deep,
title={Deep face super-resolution with iterative collaboration between attentive recovery and landmark estimation},
author={Ma, Cheng and Jiang, Zhenyu and Rao, Yongming and Lu, Jiwen and Zhou, Jie},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={5569--5578},
year={2020}
}
在 RGB 通道上进行评估,在评估之前裁剪每个边界中的 scale
像素。
我们使用 PSNR
和 SSIM
作为指标。
在 dic_gan_x8c48b6_g4_150k_CelebAHQ
的日志中,DICGAN 在 CelebA-HQ 测试集的前9张图片上进行了验证,因此下表中的 PSNR/SSIM
与日志数据不同。
算法 | scale | CelebA-HQ | 下载 |
---|---|---|---|
dic_x8c48b6_g4_150k_CelebAHQ | x8 | 25.2319 / 0.7422 | 模型 | 日志 |
dic_gan_x8c48b6_g4_150k_CelebAHQ | x8 | 23.6241 / 0.6721 | 模型 | 日志 |
EDSR (CVPR’2017)¶
EDSR (CVPR'2017)
@inproceedings{lim2017enhanced,
title={Enhanced deep residual networks for single image super-resolution},
author={Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Mu Lee, Kyoung},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
pages={136--144},
year={2017}
}
在 RGB 通道上进行评估,在评估之前裁剪每个边界中的 scale
像素。
我们使用 PSNR
和 SSIM
作为指标。
算法 | Set5 | Set14 | DIV2K | 下载 |
---|---|---|---|---|
edsr_x2c64b16_1x16_300k_div2k | 35.7592 / 0.9372 | 31.4290 / 0.8874 | 34.5896 / 0.9352 | 模型 | 日志 |
edsr_x3c64b16_1x16_300k_div2k | 32.3301 / 0.8912 | 28.4125 / 0.8022 | 30.9154 / 0.8711 | 模型 | 日志 |
edsr_x4c64b16_1x16_300k_div2k | 30.2223 / 0.8500 | 26.7870 / 0.7366 | 28.9675 / 0.8172 | 模型 | 日志 |
EDVR (CVPRW’2019)¶
EDVR (CVPRW'2019)
@InProceedings{wang2019edvr,
author = {Wang, Xintao and Chan, Kelvin C.K. and Yu, Ke and Dong, Chao and Loy, Chen Change},
title = {EDVR: Video restoration with enhanced deformable convolutional networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
month = {June},
year = {2019},
}
在 RGB 通道上进行评估。
我们使用 PSNR
和 SSIM
作为指标。
算法 | REDS4 | 下载 |
---|---|---|
edvrm_wotsa_x4_8x4_600k_reds | 30.3430 / 0.8664 | 模型 | 日志 |
edvrm_x4_8x4_600k_reds | 30.4194 / 0.8684 | 模型 | 日志 |
edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4 | 31.0010 / 0.8784 | 模型 | 日志 |
edvrl_c128b40_8x8_lr2e-4_600k_reds4 | 31.0467 / 0.8793 | 模型 | 日志 |
ESRGAN (ECCVW’2018)¶
ESRGAN (ECCVW'2018)
@inproceedings{wang2018esrgan,
title={Esrgan: Enhanced super-resolution generative adversarial networks},
author={Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Change Loy, Chen},
booktitle={Proceedings of the European Conference on Computer Vision Workshops(ECCVW)},
pages={0--0},
year={2018}
}
在 RGB 通道上进行评估,在评估之前裁剪每个边界中的 scale
像素。
我们使用 PSNR
和 SSIM
作为指标。
算法 | Set5 | Set14 | DIV2K | 下载 |
---|---|---|---|---|
esrgan_psnr_x4c64b23g32_1x16_1000k_div2k | 30.6428 / 0.8559 | 27.0543 / 0.7447 | 29.3354 / 0.8263 | 模型 | 日志 |
esrgan_x4c64b23g32_1x16_400k_div2k | 28.2700 / 0.7778 | 24.6328 / 0.6491 | 26.6531 / 0.7340 | 模型 | 日志 |
GLEAN (CVPR’2021)¶
GLEAN (CVPR'2021)
@InProceedings{chan2021glean,
author = {Chan, Kelvin CK and Wang, Xintao and Xu, Xiangyu and Gu, Jinwei and Loy, Chen Change},
title = {GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution},
booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},
year = {2021}
}
有关训练和测试中使用的元信息,请参阅此处。 结果在 RGB 通道上进行评估。
算法 | PSNR | 下载 |
---|---|---|
glean_cat_8x | 23.98 | 模型 | 日志 |
glean_ffhq_16x | 26.91 | 模型 | 日志 |
glean_cat_16x | 20.88 | 模型 | 日志 |
IconVSR (CVPR’2021)¶
IconVSR (CVPR'2021)
@InProceedings{chan2021basicvsr,
author = {Chan, Kelvin CK and Wang, Xintao and Yu, Ke and Dong, Chao and Loy, Chen Change},
title = {BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond},
booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},
year = {2021}
}
对于 REDS4,我们对 RGB 通道进行评估。对于其他数据集,我们对 Y 通道进行评估。我们使用 PSNR
和 SSIM
作为指标。
IconVSR 组件的预训练权重可以在这里找到:SPyNet,用于 REDS 的 EDVR-M,以及 用于 Vimeo-90K 的 EDVR-M。
算法 | REDS4 (BIx4) PSNR/SSIM (RGB) |
Vimeo-90K-T (BIx4) PSNR/SSIM (Y) |
Vid4 (BIx4) PSNR/SSIM (Y) |
UDM10 (BDx4) PSNR/SSIM (Y) |
Vimeo-90K-T (BDx4) PSNR/SSIM (Y) |
Vid4 (BDx4) PSNR/SSIM (Y) |
下载 |
---|---|---|---|---|---|---|---|
iconvsr_reds4 | 31.6926/0.8951 | 36.4983/0.9416 | 27.4809/0.8354 | 35.3377/0.9471 | 34.4299/0.9287 | 25.2110/0.7732 | 模型 | 日志 |
iconvsr_vimeo90k_bi | 30.3452/0.8659 | 37.3729/0.9467 | 27.4238/0.8297 | 34.2595/0.9398 | 34.5548/0.9295 | 24.6666/0.7491 | 模型 | 日志 |
iconvsr_vimeo90k_bd | 29.0150/0.8465 | 34.6780/0.9339 | 26.3109/0.8028 | 40.0640/0.9697 | 37.7573/0.9517 | 28.2464/0.8612 | 模型 | 日志 |
LIIF (CVPR’2021)¶
LIIF (CVPR'2021)
@inproceedings{chen2021learning,
title={Learning continuous image representation with local implicit image function},
author={Chen, Yinbo and Liu, Sifei and Wang, Xiaolong},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={8628--8638},
year={2021}
}
算法 | scale | Set5 PSNR / SSIM |
Set14 PSNR / SSIM |
DIV2K PSNR / SSIM |
下载 |
---|---|---|---|---|---|
liif_edsr_norm_x2-4_c64b16_g1_1000k_div2k | x2 | 35.7148 / 0.9367 | 31.5936 / 0.8889 | 34.5896 / 0.9352 | 模型 | 日志 |
△ | x3 | 32.3596 / 0.8914 | 28.4475 / 0.8040 | 30.9154 / 0.8720 | △ |
△ | x4 | 30.2583 / 0.8513 | 26.7867 / 0.7377 | 29.0048 / 0.8183 | △ |
liif_edsr_norm_c64b16_g1_1000k_div2k | x2 | 35.7120 / 0.9365 | 31.6106 / 0.8891 | 34.6401 / 0.9353 | 模型 | 日志 |
△ | x3 | 32.3655 / 0.8913 | 28.4605 / 0.8039 | 30.9597 / 0.8711 | △ |
△ | x4 | 30.2668 / 0.8511 | 26.8093 / 0.7377 | 29.0059 / 0.8183 | △ |
△ | x6 | 27.0907 / 0.7775 | 24.7129 / 0.6438 | 26.7694 / 0.7422 | △ |
△ | x12 | 22.9046 / 0.6255 | 21.5378 / 0.5088 | 23.7269 / 0.6373 | △ |
△ | x18 | 20.8445 / 0.5390 | 20.0215 / 0.4521 | 22.1920 / 0.5947 | △ |
△ | x24 | 19.7305 / 0.5033 | 19.0703 / 0.4218 | 21.2025 / 0.5714 | △ |
△ | x30 | 18.6646 / 0.4818 | 18.0210 / 0.3905 | 20.5022 / 0.5568 | △ |
注:
△ 指同上。
这两个配置仅在 testing pipeline 上有所不同。 所以他们使用相同的检查点。
数据根据 EDSR 进行正则化。
在 RGB 通道上进行评估,在评估之前裁剪每个边界中的
scale
像素。
RDN (CVPR’2018)¶
RDN (CVPR'2018)
@inproceedings{zhang2018residual,
title={Residual dense network for image super-resolution},
author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={2472--2481},
year={2018}
}
在 RGB 通道上进行评估,在评估之前裁剪每个边界中的 scale
像素。
我们使用 PSNR
和 SSIM
作为指标。
算法 | Set5 | Set14 | DIV2K | 下载 |
---|---|---|---|---|
rdn_x2c64b16_g1_1000k_div2k | 35.9883 / 0.9385 | 31.8366 / 0.8920 | 34.9392 / 0.9380 | 模型 | 日志 |
rdn_x3c64b16_g1_1000k_div2k | 32.6051 / 0.8943 | 28.6338 / 0.8077 | 31.2153 / 0.8763 | 模型 | 日志 |
rdn_x4c64b16_g1_1000k_div2k | 30.4922 / 0.8548 | 26.9570 / 0.7423 | 29.1925 / 0.8233 | 模型 | 日志 |
RealBasicVSR (CVPR’2022)¶
RealBasicVSR (CVPR'2022)
@InProceedings{chan2022investigating,
author = {Chan, Kelvin C.K. and Zhou, Shangchen and Xu, Xiangyu and Loy, Chen Change},
title = {RealBasicVSR: Investigating Tradeoffs in Real-World Video Super-Resolution},
booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},
year = {2022}
}
在 Y 通道上评估。 计算 NRQM、NIQE 和 PI 的代码可以在这里找到。我们使用 MATLAB 官方代码计算 BRISQUE。
算法 | NRQM (Y) | NIQE (Y) | PI (Y) | BRISQUE (Y) | Download |
---|---|---|---|---|---|
realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds | 6.0477 | 3.7662 | 3.8593 | 29.030 | model/log |
Real-ESRGAN (ICCVW’2021)¶
Real-ESRGAN (ICCVW'2021)
@inproceedings{wang2021real,
title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic data},
author={Wang, Xintao and Xie, Liangbin and Dong, Chao and Shan, Ying},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},
pages={1905--1914},
year={2021}
}
在 RGB 通道上进行评估,指标为 PSNR/SSIM
。
算法 | Set5 | 下载 |
---|---|---|
realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost | 28.0297/0.8236 | 模型/日志 |
SRCNN (TPAMI’2015)¶
SRCNN (TPAMI'2015)
@article{dong2015image,
title={Image super-resolution using deep convolutional networks},
author={Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou},
journal={IEEE transactions on pattern analysis and machine intelligence},
volume={38},
number={2},
pages={295--307},
year={2015},
publisher={IEEE}
}
在 RGB 通道上进行评估,在评估之前裁剪每个边界中的 scale
像素。
我们使用 PSNR
和 SSIM
作为指标。
算法 | Set5 | Set14 | DIV2K | 下载 |
---|---|---|---|---|
srcnn_x4k915_1x16_1000k_div2k | 28.4316 / 0.8099 | 25.6486 / 0.7014 | 27.7460 / 0.7854 | 模型 | 日志 |
SRGAN (CVPR’2016)¶
SRGAN (CVPR'2016)
@inproceedings{ledig2016photo,
title={Photo-realistic single image super-resolution using a generative adversarial network},
author={Ledig, Christian and Theis, Lucas and Husz{\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew and Tejani, Alykhan and Totz, Johannes and Wang, Zehan},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
year={2016}
}
在 RGB 通道上进行评估,在评估之前裁剪每个边界中的 scale
像素。
我们使用 PSNR
和 SSIM
作为指标。
算法 | Set5 | Set14 | DIV2K | 下载 |
---|---|---|---|---|
msrresnet_x4c64b16_1x16_300k_div2k | 30.2252 / 0.8491 | 26.7762 / 0.7369 | 28.9748 / 0.8178 | 模型 | 日志 |
srgan_x4c64b16_1x16_1000k_div2k | 27.9499 / 0.7846 | 24.7383 / 0.6491 | 26.5697 / 0.7365 | 模型 | 日志 |
TDAN (CVPR’2020)¶
TDAN (CVPR'2020)
@InProceedings{tian2020tdan,
title={TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution},
author={Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
booktitle = {Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
year = {2020}
}
在 RGB 通道上进行评估,在评估之前裁剪每个边界中的8像素。
我们使用 PSNR
和 SSIM
作为指标。
算法 | Vid4 (BIx4) | SPMCS-30 (BIx4) | Vid4 (BDx4) | SPMCS-30 (BDx4) | 下载 |
---|---|---|---|---|---|
tdan_vimeo90k_bix4_ft_lr5e-5_400k | 26.49/0.792 | 30.42/0.856 | 25.93/0.772 | 29.69/0.842 | 模型 | 日志 |
tdan_vimeo90k_bdx4_ft_lr5e-5_800k | 25.80/0.784 | 29.56/0.851 | 26.87/0.815 | 30.77/0.868 | 模型 | 日志 |
训练
训练说明
您可以使用以下命令来训练模型。
./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]
TDAN 训练有两个阶段。
阶段 1: 以更大的学习率训练 (1e-4)
./tools/dist_train.sh configs/restorers/tdan/tdan_vimeo90k_bix4_lr1e-4_400k.py 8
阶段 2: 以较小的学习率进行微调 (5e-5)
./tools/dist_train.sh configs/restorers/tdan/tdan_vimeo90k_bix4_ft_lr5e-5_400k.py 8
更多细节可以参考 getting_started 中的 Train a model 部分。
测试
测试说明
您可以使用以下命令来测试模型。
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]
示例:使用 bicubic
下采样在 SPMCS-30 上测试 TDAN。
python tools/test.py configs/restorers/tdan/tdan_vimeo90k_bix4_ft_lr5e-5_400k.py checkpoints/SOME_CHECKPOINT.pth --save_path outputs/
更多细节可以参考 getting_started 中的 Inference with pretrained models 部分。
TOFlow (IJCV’2019)¶
TOFlow (IJCV'2019)
@article{xue2019video,
title={Video enhancement with task-oriented flow},
author={Xue, Tianfan and Chen, Baian and Wu, Jiajun and Wei, Donglai and Freeman, William T},
journal={International Journal of Computer Vision},
volume={127},
number={8},
pages={1106--1125},
year={2019},
publisher={Springer}
}
在 RGB 通道上进行评估。
我们使用 PSNR
和 SSIM
作为指标。
算法 | Vid4 | 下载 |
---|---|---|
tof_x4_vimeo90k_official | 24.4377 / 0.7433 | 模型 |
TTSR (CVPR’2020)¶
TTSR (CVPR'2020)
@inproceedings{yang2020learning,
title={Learning texture transformer network for image super-resolution},
author={Yang, Fuzhi and Yang, Huan and Fu, Jianlong and Lu, Hongtao and Guo, Baining},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={5791--5800},
year={2020}
}
在 RGB 通道上进行评估,在评估之前裁剪每个边界中的 scale
像素。
我们使用 PSNR
和 SSIM
作为指标。
算法 | scale | CUFED | 下载 |
---|---|---|---|
ttsr-rec_x4_c64b16_g1_200k_CUFED | x4 | 25.2433 / 0.7491 | 模型 | 日志 |
ttsr-gan_x4_c64b16_g1_500k_CUFED | x4 | 24.6075 / 0.7234 | 模型 | 日志 |