Abstract
Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.
A. Ignatov, R. Timofte, M. Denna and A. Younes—Are the Mobile AI & AIM 2022 challenge organizers. The other authors participated in the challenge.
Appendix A contains the authors’ team names and affiliations.
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References
Abdelhamed, A., Afifi, M., Timofte, R., Brown, M.S.: Ntire 2020 challenge on real image denoising: dataset, methods and results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 496–497 (2020)
Abdelhamed, A., Timofte, R., Brown, M.S.: Ntire 2019 challenge on real image denoising: Methods and results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)
Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126–135 (2017)
Anwar, S., Hwang, K., Sung, W.: Structured pruning of deep convolutional neural networks. ACM J. Emerg. Technol. Comput. Syst. (JETC) 13(3), 1–18 (2017)
Anwar, S., Sung, W.: Compact deep convolutional neural networks with coarse pruning. arXiv preprint arXiv:1610.09639 (2016)
Ayazoglu, M.: Extremely lightweight quantization robust real-time single-image super resolution for mobile devices. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)
Ayazoglu, M., Bilecen, B.B.: XCAT - lightweight quantized single image super-resolution using heterogeneous group convolutions and cross concatenation. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2022)
Bhardwaj, K., et al.: Collapsible linear blocks for super-efficient super resolution. Proc. Mach. Learn. Syst. 4, 529–547 (2022)
Cai, J., Gu, S., Timofte, R., Zhang, L.: Ntire 2019 challenge on real image super-resolution: methods and results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)
Cai, Y., Yao, Z., Dong, Z., Gholami, A., Mahoney, M.W., Keutzer, K.: Zeroq: A novel zero shot quantization framework. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13169–13178 (2020)
Chiang, C.M., et al.: Deploying image deblurring across mobile devices: a perspective of quality and latency. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 502–503 (2020)
Conde, M.V., Timofte, R., et al.: Reversed image signal processing and RAW reconstruction. AIM 2022 Challenge Report. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2022)
Ding, X., Zhang, X., Han, J., Ding, G.: Diverse branch block: Building a convolution as an inception-like unit. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10886–10895 (2021)
Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: RepVGG: making VGG-style convnets great again. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13733–13742 (2021)
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)
Dozat, T.: Incorporating nesterov momentum into Adam (2016)
Du, Z., Liu, D., Liu, J., Tang, J., Wu, G., Fu, L.: Fast and memory-efficient network towards efficient image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 853–862 (2022)
Du, Z., Liu, J., Tang, J., Wu, G.: Anchor-based plain net for mobile image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)
Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Comput. Graphics Appl. 22(2), 56–65 (2002)
Gendy, G., nabil sabor, Hou, J., He, G.: Real-time channel mixing net for mobile image super-resolution. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2022)
Howard, A., et al.: Searching for mobilenetv3. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1314–1324 (2019)
Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197–5206 (2015)
Ignatov, A., Byeoung-su, K., Timofte, R.: Fast camera image denoising on mobile GPUs with deep learning, mobile AI 2021 challenge: Report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)
Ignatov, A., Chiang, J., Kuo, H.K., Sycheva, A., Timofte, R.: Learned smartphone ISP on mobile NPUs with deep learning, mobile AI 2021 challenge: report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)
Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., Van Gool, L.: DSLR-quality photos on mobile devices with deep convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3277–3285 (2017)
Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., Van Gool, L.: WESPE: weakly supervised photo enhancer for digital cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 691–700 (2018)
Ignatov, A., Malivenko, G., Plowman, D., Shukla, S., Timofte, R.: Fast and accurate single-image depth estimation on mobile devices, mobile AI 2021 challenge: report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)
Ignatov, A., Malivenko, G., Timofte, R.: Fast and accurate quantized camera scene detection on smartphones, mobile AI 2021 challenge: report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)
Ignatov, A., et al.: PyNet-V2 mobile: efficient on-device photo processing with neural networks. In: 2021 26th International Conference on Pattern Recognition (ICPR). IEEE (2022)
Ignatov, A., Malivenko, G., Timofte, R., et al.: Efficient single-image depth estimation on mobile devices, mobile AI & aim 2022 challenge: Report. In: European Conference on Computer Vision (2022)
Ignatov, A., Patel, J., Timofte, R.: Rendering natural camera bokeh effect with deep learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 418–419 (2020)
Ignatov, A., et al.: Aim 2019 challenge on bokeh effect synthesis: Methods and results. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3591–3598. IEEE (2019)
Ignatov, A., Romero, A., Kim, H., Timofte, R.: Real-time video super-resolution on smartphones with deep learning, mobile AI 2021 challenge: report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)
Ignatov, A., et al.: MicroISP: processing 32mp photos on mobile devices with deep learning. In: European Conference on Computer Vision (2022)
Ignatov, A., Timofte, R.: Ntire 2019 challenge on image enhancement: methods and results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)
Ignatov, A., et al.: Power efficient video super-resolution on mobile NPUs with deep learning, mobile AI & aim 2022 challenge: report. In: European Conference on Computer Vision (2022)
Ignatov, A., et al.: AI benchmark: running deep neural networks on android smartphones. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 288–314. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_19
Ignatov, A., Timofte, R., Denna, M., Younes, A.: Real-time quantized image super-resolution on mobile NPUs, mobile AI 2021 challenge: report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)
Ignatov, A., et al.: Aim 2019 challenge on raw to RGB mapping: methods and results. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3584–3590. IEEE (2019)
Ignatov, A., et al.: Ai benchmark: all about deep learning on smartphones in 2019. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3617–3635. IEEE (2019)
Ignatov, A., et al.: AIM 2020 challenge on rendering realistic Bokeh. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12537, pp. 213–228. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-67070-2_13
Ignatov, A., et al.: Pirm challenge on perceptual image enhancement on smartphones: Report. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)
Ignatov, A., et al.: Aim 2020 challenge on learned image signal processing pipeline. arXiv preprint arXiv:2011.04994 (2020)
Ignatov, A., Timofte, R., et al.: Learned smartphone ISP on mobile GPUs with deep learning, mobile AI & aim 2022 challenge: report. In: European Conference on Computer Vision (2022)
Ignatov, A., Timofte, R., et al.: Realistic bokeh effect rendering on mobile GPUs, mobile AI & aim 2022 challenge: report (2022)
Ignatov, A., Van Gool, L., Timofte, R.: Replacing mobile camera ISP with a single deep learning model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 536–537 (2020)
Ignatov, D., Ignatov, A.: Controlling information capacity of binary neural network. Pattern Recogn. Lett. 138, 276–281 (2020)
Inc., S.: https://www.synaptics.com/technology/edge-computing
Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP: Graph. Models Image Process. 53(3), 231–239 (1991)
Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2704–2713 (2018)
Jain, S.R., Gural, A., Wu, M., Dick, C.H.: Trained quantization thresholds for accurate and efficient fixed-point inference of deep neural networks. arXiv preprint arXiv:1903.08066 (2019)
Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)
Kinli, F.O., Mentes, S., Ozcan, B., Kirac, F., Timofte, R., et al.: Aim 2022 challenge on Instagram filter removal: methods and results. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2022)
Kong, F., et al.: Residual local feature network for efficient super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 766–776 (2022)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)
Li, Y., Gu, S., Gool, L.V., Timofte, R.: Learning filter basis for convolutional neural network compression. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5623–5632 (2019)
Li, Y., et al.: Ntire 2022 challenge on efficient super-resolution: methods and results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1062–1102 (2022)
Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136–144 (2017)
Liu, Z., et al.: Metapruning: meta learning for automatic neural network channel pruning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3296–3305 (2019)
Liu, Z., Wu, B., Luo, W., Yang, X., Liu, W., Cheng, K.-T.: Bi-real net: enhancing the performance of 1-Bit CNNs with improved representational capability and advanced training algorithm. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 747–763. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_44
Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)
Lugmayr, A., Danelljan, M., Timofte, R.: Ntire 2020 challenge on real-world image super-resolution: Methods and results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 494–495 (2020)
Luo, Z., et al.: Fast nearest convolution for real-time efficient image super-resolution. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2022)
Obukhov, A., Rakhuba, M., Georgoulis, S., Kanakis, M., Dai, D., Van Gool, L.: T-basis: a compact representation for neural networks. In: International Conference on Machine Learning, pp. 7392–7404. PMLR (2020)
Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. IEEE Signal Process. Mag. 20(3), 21–36 (2003)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Tan, M., et al.: MNASnet: platform-aware neural architecture search for mobile. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2820–2828 (2019)
TensorFlow-Lite: https://www.tensorflow.org/lite
Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L.: Ntire 2017 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 114–125 (2017)
Timofte, R., De Smet, V., Van Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1920–1927 (2013)
Timofte, R., De Smet, V., Van Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 111–126. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16817-3_8
Timofte, R., Gu, S., Wu, J., Van Gool, L.: Ntire 2018 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 852–863 (2018)
Timofte, R., Rothe, R., Van Gool, L.: Seven ways to improve example-based single image super resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1865–1873 (2016)
Uhlich, S., et al.: Mixed precision DNNs: All you need is a good parametrization. arXiv preprint arXiv:1905.11452 (2019)
Wan, A., et al.: Fbnetv2: differentiable neural architecture search for spatial and channel dimensions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12965–12974 (2020)
Wang, H., Chen, P., Zhuang, B., Shen, C.: Fully quantized image super-resolution networks. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 639–647 (2021)
Wang, Y., et al.: Towards compact single image super-resolution via contrastive self-distillation. arXiv preprint arXiv:2105.11683 (2021)
Wu, B., et al.: FBNet: hardware-aware efficient convnet design via differentiable neural architecture search. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10734–10742 (2019)
Yang, C.Y., Yang, M.H.: Fast direct super-resolution by simple functions. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 561–568 (2013)
Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)
Yang, J., et al.: Quantization networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7308–7316 (2019)
Yang, R., Timofte, R., et al.: Aim 2022 challenge on super-resolution of compressed image and video: Dataset, methods and results. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2022)
Zagoruyko, S., Komodakis, N.: DiracNets: Training very deep neural networks without skip-connections. arXiv preprint arXiv:1706.00388 (2017)
Zhang, K., Gu, S., Timofte, R.: Ntire 2020 challenge on perceptual extreme super-resolution: methods and results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 492–493 (2020)
Zhuang, J., et al.: Adabelief optimizer: adapting stepsizes by the belief in observed gradients. Adv. Neural. Inf. Process. Syst. 33, 18795–18806 (2020)
Acknowledgements
We thank the sponsors of the Mobile AI and AIM 2022 workshops and challenges: Synaptics Inc., AI Witchlabs, MediaTek, Huawei, Reality Labs, OPPO, Raspberry Pi, ETH Zürich (Computer Vision Lab) and University of Würzburg (Computer Vision Lab).
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Authors and Affiliations
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Editors and Affiliations
A Teams and Affiliations
A Teams and Affiliations
1.1 Mobile AI 2022 Team
Title:
Mobile AI 2022 Image Super-Resolution Challenge
Members:
Andrey Ignatov\(^{1,2}\) (andrey@vision.ee.ethz.ch), Radu Timofte\(^{1,2,3}\) (radu.timofte @vision.ee.ethz.ch), Maurizio Denna\(^4\) (maurizio.denna@synaptics.com), Abdel Younes\(^4\) (abdel.younes@synaptics.com)
Affiliations:
\(^1\) Computer Vision Lab, ETH Zürich, Switzerland
\(^2\) AI Witchlabs, Switzerland
\(^3\) University of Würzburg, Germany
\(^4\) Synaptics Europe, Switzerland
1.2 Z6
Title:
Skip-Concatenated Image Super Resolution Network (SCSRN) for Mobile Devices
Members:
Ganzorig Gankhuyag\(^1\) (gnzrg25@gmail.com), Jingang Huh\(^1\), Myeong Kyun Kim\(^1\), Kihwan Yoon\(^1\), Hyeon-Cheol Moon\(^1\), Seungho Lee\(^1\), Yoonsik Choe\(^2\), Jinwoo Jeong\(^1\), Sungjei Kim\(^1\)
Affiliations:
\(^1\) Korea Electronics Technology Institute (KETI), South Korea
\(^2\) Yonsei University, South Korea
1.3 TCLResearchEurope
Title:
RPQ—Extremely Efficient Super-Resolution Network Via Reparametrization, Pruning and Quantization
Members:
Maciej Smyl (maciej.smyl@gmail.com), Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma
Affiliations:
TCL Research Europe, Poland
1.4 ECNUSR
Title:
PureConvSR: lightweight pure convolutional neural network with equivalent transformation
Members:
Jiahao Chao (51215902006@stu.ecnu.edu.cn), Zhou Zhou, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng
Affiliations:
East China Normal University, China
1.5 LCVG
Title:
HOPN: A Hardware Optimized Plain Network for Mobile Image Super-Resolution
Members:
Zhengyang Zhuge (zyoung2333@gmail.com), Chenghua Li
Affiliations:
Institute of Automation, Chinese Academy of Sciences, China
1.6 BOE-IOT-AIBD
Title:
Lightweight Quantization CNNNet for Mobile Image Super-Resolution
Members:
Dan Zhu (zhudan@boe.com.cn), Mengdi Sun, Ran Duan, Yan Gao
Affiliations:
BOE Technology Group Co., Ltd., China
1.7 NJUST
Title:
EMSRNet: An Efficient ConvNet for Real-time Image Super-resolution on Mobile Devices
Members:
Lingshun Kong (konglingshun@njust.edu.cn), Long Sun, Xiang Li, Xingdong Zhang, Jiawei Zhang, Yaqi Wu, Jinshan Pan
Affiliations:
Nanjing University of Science and Technology, China
1.8 Antins_cv
Title:
Extremely Light-Weight Dual-Branch Network for Real Time Image Super Resolution
Members:
Gaocheng Yu (yugaocheng.ygc@antgroup.com), Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang
Affiliations:
Ant Group, China
1.9 GenMedia Group
Title:
SkipSkip Video Super-Resolution
Members:
Hojin Cho (jin@gengen.ai), Steve Kim
Affiliations:
GenGenAI, South Korea
1.10 Vccip
Title:
Diverse Branch Re-Parameterizable Net for Mobile Image Super-Resolution
Members:
Huaen Li (huaenli@mail.hfut.edu.cn), Yanbo Ma
Affiliations:
Hefei University of Technology, China
1.11 MegSR
Title:
Fast Nearest Convolution for Real-Time Image Super-Resolution [64]
Members:
Ziwei Luo (ziwei.ro@gmail.com), Youwei Li, Lei Yu, Zhihong Wen, Qi Wu, Haoqiang Fan, Shuaicheng Liu
Affiliations:
Megvii Technology, China
University of Electronic Science and Technology of China (UESTC), China
1.12 DoubleZ
Title:
Fast Image Super-Resolution Model
Members:
Lize Zhang (lzzhang_98@stu.xidian.edu.cn), Zhikai Zong
Affiliations:
Xidian University, China
Qingdao Hi-image Technologies Co., Ltd., China
1.13 Jeremy Kwon
Title:
S2R2: Salus Super-Resolution Research
Members:
Jeremy Kwon (alan.jaeger0@gmail.com)
Affiliations:
None, South Korea
1.14 Lab216
Title:
Lightweight Asymmetric Super-Resolution Network with Contrastive Quantized-aware Training
Members:
Junxi Zhang (sissie_zhang@whu.edu.cn), Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu, Zhenzhong Chen
Affiliations:
Wuhan University, China
1.15 TOVB
Title:
RBPN: Repconv-based Plain Net for Mobile Image Super-Resolution
Members:
Gen Li (leegeun@yonsei.ac.kr), Yuanfan Zhang, Lei Sun
Affiliations:
None, China
1.16 Samsung Research
Title:
ABPN++: Anchor-based Plain Net for Mobile Image Super-Resolution with Pre-training
Members:
Dafeng Zhang (dfeng.zhang@samsung.com)
Affiliations:
Samsung Research, China
1.17 Rtsisr2022
Title:
Re-parameterized Anchor-based Plain Network
Members:
Neo Yang (296859095@qq.com), Fitz Liu, Jerry Zhao
Affiliations:
None, China
1.18 Aselsan Research
Title:
XCAT - Lightweight Single Image Super Resolution Network with Cross Concatenated Heterogeneous Group Convolutions [7]
Members:
Mustafa Ayazoglu (mayazoglu@aselsan.com.tr), Bahri Batuhan Bilecen
Affiliations:
Aselsan Corporation, Turkey
1.19 Klab_SR
Title:
Deeper and narrower SR Model
Members:
Shota Hirose (syouta.hrs@akane.waseda.jp), Kasidis Arunruangsirilert, Luo Ao
Affiliations:
Waseda University, Japan
1.20 TCL Research HK
Title:
Anchor-collapsed Super Resolution for Mobile Devices
Members:
Ho Chun Leung (hcleung@tcl.com), Andrew Wei, Jie Liu, Qiang Liu, Dahai Yu
Affiliations:
TCL Corporate Research, China
1.21 RepGSR
Title:
Super Lightweight Super-resolution Based on Ghost Features with Re-parameterization
Members:
Ao Li (liao@cqu.edu.cn), Lei Luo, Ce Zhu
Affiliations:
University of Electronic Science and Technology, China
1.22 ICL
Title:
Neural Network Quantization With Reparametrization And Kernel Range Regularization
Members:
Seongmin Hong (smhongok@snu.ac.kr), Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun
Affiliations:
Intelligent Computational Imaging Lab, Seoul National University, South Korea
1.23 Just A try
Title:
Plain net for realtime Super-resolution
Members:
Ruiyuan He (164643209@qq.com), Xuhao Jiang
Affiliations:
None, China
1.24 Bilibili AI
Title:
A Robust Anchor-based network for Mobile Super-Resolution
Members:
Haihang Ruan (hhruan@mail.sim.ac.cn), Xinjian Zhang, Jing Liu
Affiliations:
Bilibili Inc., China
1.25 MobileSR
Title:
Real-Time Channel Mixing Net for Mobile Image Super-Resolution [21]
Members:
Garas Gendy\(^1\) (garasgaras@yahoo.com), Nabil Sabor\(^2\), Jingchao Hou\(^1\), Guanghui He\(^1\)
Affiliations:
\(^1\) Shanghai Jiao Tong University, China
\(^2\) Assiut University, Egypt
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Ignatov, A. et al. (2023). Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 Challenge: Report. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13803. Springer, Cham. https://doi.org/10.1007/978-3-031-25066-8_5
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