NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results
Abstract
This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution.
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Temporal factorization decomposes spatiotemporal attention into sequential spatial and temporal attention steps to improve efficiency.
“Temporal factorization (as in TimeSformer [32]) decomposes spatiotemporal attention into sequential spatial and temporal attention steps to improve efficiency.”
From A Comprehensive Survey of Transformer-Based Models for Video Anomaly Detection in Surveillance Systems (2025) · cites this paper as [32]
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Self-citationMulti-level Dispersion Residual Network for Efficient Image Super-Resolution
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Self-citationCollapsible Linear Blocks for Super-Efficient Super ResolutionPreprint
Bhardwaj, K., Milosavljevic, M., O'Neil, L., Gope, D., Matas, R., Chalfin, A., Suda, N., Meng, L., & Loh, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.09404 · Cited by 36 · see citing works
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Self-citationMixer-based Local Residual Network for Lightweight Image Super-resolution
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Self-citationNTIRE 2023 Video Colorization Challenge
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Self-citationNTIRE 2023 Challenge on Stereo Image Super-Resolution: Methods and Results
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Self-citationNTIRE 2023 Challenge on HR Depth from Images of Specular and Transparent Surfaces
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Self-citationEfficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report
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Self-citationNTIRE 2023 Quality Assessment of Video Enhancement Challenge
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Self-citationLens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challenge Report
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Self-citationNTIRE 2023 Challenge on Night Photography Rendering
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Self-citationHuman Guided Ground-Truth Generation for Realistic Image Super-Resolution
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Self-citationThe Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures
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Self-citationReal-Time Channel Mixing Net for Mobile Image Super-Resolution
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Self-citationImage Super-Resolution using Efficient Striped Window TransformerPreprint
Shi, J., Li, H., Liu, T., Liu, Y., Zhang, M., Zhu, J., Zheng, L., & Weng, S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.09869 · Cited by 15 · see citing works
Self-citationA Single Residual Network with ESA Modules and Distillation
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Large Kernel Distillation Network for Efficient Single Image Super-Resolution
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Self-citationDIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution
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Self-citationReparameterized Residual Feature Network For Lightweight Image Super-Resolution
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Self-citationNTIRE 2023 Challenge on Image Super-Resolution (×4): Methods and Results
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Self-citationThe Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
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NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results
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Self-citationNTIRE 2023 Challenge on Image Denoising: Methods and Results
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Self-citationNTIRE 2023 Image Shadow Removal Challenge Report
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Self-citationLarge Kernel Frequency-enhanced Network for Efficient Single Image Super-Resolution
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RefConv: Reparameterized Refocusing Convolution for Powerful ConvNets
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Multi-level Dispersion Residual Network for Efficient Image Super-Resolution
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Self-citationCao, M., Mou, C., Yu, F., Wang, X., Zheng, Y., Zhang, J., Dong, C., Li, G., Shan, Y., Timofte, R., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00174 · Cited by 34 · see citing works
Self-citationNTIRE 2023 Challenge on Stereo Image Super-Resolution: Methods and Results
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Self-citationMixer-based Local Residual Network for Lightweight Image Super-resolution
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Self-citationNTIRE 2023 Video Colorization Challenge
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Self-citationNTIRE 2023 Challenge on HR Depth from Images of Specular and Transparent Surfaces
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Self-citationEfficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report
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Self-citationA Survey of Deep Learning Video Super-Resolution
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NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report
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Self-citationNTIRE 2023 Quality Assessment of Video Enhancement Challenge
Liu, X., Min, X., Sun, W., Zhang, Y., Zhang, K., Timofte, R., Zhai, G., Gao, Y., Cao, Y., Kou, T., et al. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00158 · Cited by 22 · see citing works
Self-citationLens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challenge Report
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Self-citationNTIRE 2023 Challenge on Night Photography Rendering
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Self-citationIntelligent biosensors for human movement rehabilitation and intention recognition
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Asymmetric Information Distillation Network for Lightweight Super Resolution
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CVIformer: Cross-View Interactive Transformer for Efficient Stereoscopic Image Super-Resolution
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Self-citationA Tightly Coupled AI-ISP Vision Processor
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Self-citationPerceptual super-resolution in multiple sclerosis MRI
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“…(MHSA) allows modeling of long-term dependencies across hundreds of frames, overcoming the limitations of RNN-based models such as LSTMs. • Temporal factorization (as in TimeSformer [32]) decomposes spatiotemporal attention into sequential spatial and temporal attention steps to improve efficiency.”
Adaptive Frequency Enhancement Network for Single Image Deraining
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Efficient Perceptual Image Super Resolution: AIM 2025 Study and Benchmark
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Object Acquizition of Stationary and Moving Targets
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Self-citationZhu, H., Chen, Z., & Liu, S. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3519136 · Cited by 2 · see citing works
Self-citationWavelet-driven multi-frequency signal unlocking network for image deraining
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Self-citationBroadcast-Gated Attention With Identity Adaptive Integration for Efficient Image Super-Resolution
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Self-citationNasser, S. M. M., Ramalingam, V., Verma, A., & Madhavan, N. (2026) · Energy Geoscience · doi:10.1016/j.engeos.2026.100559 · Cited by 2 · see citing works
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Expanded SPAN for Efficient Super-Resolution
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Self-citationSajid, M. S. B., Alkhrijah, Y. M., Hamid, D., Akram, M. T., Usman, S. M., Khalid, S., & Alawad, M. A. (2026) · IEEE Access · doi:10.1109/access.2026.3655585 · Cited by 1 · see citing works
Embedding Similarity Learning for Extreme License Plate Super-Resolution
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Improving Generalization of Medical Image Registration Foundation Model
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Self-citationA Study to Assess the Functional Independance and Disability Evaluvation Among Stroke Patients
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Artificial Neural Network-Powered, Driverless Vehicle Concept Development
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Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution
Li, Y., Zhang, Z., Zhao, S., Fan, J., Zhang, H., & Xu, M. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00042
Self-citationHierarchical Context Modeling Network for Landmark Recognition
Bao, X., Zheng, H. Q., Zhang, Z., Zha, Z.-J., & Wang, M. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00103
Self-citationAdaptive Student Inference Network for Efficient Single Image Super-Resolution
Miao, K., Zhang, Z., Ren, J., Zhao, M., Zhang, H., & Hong, R. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00058
Self-citationA lightweight image super-resolution network based on large receptive field information distillation
wu, C., Wang, K., shen, Z., qu, J., & li, Q. (2024) · International Conference on Informatics, Networking and Computing (ICINC) · doi:10.1117/12.3024646
Self-citationQingqing, 王. W., Yuelan, 辛. X., Jia, 赵. Z., Jiang, 郭. G., & Haochen, 王. W. (2024) · Laser & Optoelectronics Progress · doi:10.3788/lop232053
An Ultra-Lightweight Image Super-Resolution NetworkPreprint
Bai, H., & Sang, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4872121
Hybrid-Domain Attention Dense Network for Efficient Image Super-Resolution
He, Y., He, J., Xue, M., Zhong, S., & Zhou, M. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425540035
Lightweight Autoencoder with Hierarchical Priors for Learned Image Compression
Zhou, J., Huang, Q., Tian, X., Zhou, L., Yang, Y., & Xiang, J. (2024) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc64274.2024.00015
The Impact of AI on Digital Marketing Across Various Industries
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- Authors
- Li, Y., Zhang, Y., Timofte, R., Van Gool, L., Yu, L., Li, Y., Li, X., Jiang, T., Wu, Q., Han, M., Lin, W., Jiang, C., Luo, J., Fan, H., Liu, S., Wang, Y., Cai, M., Li, M., Zhang, Y., Fan, X.-J., Sheng, Y., Mao, Y., Zhang, N., Wang, Q., Zheng, M., Sun, L., Pan, J., Dong, J., Tang, J., Yang, Z., Wang, Y., Pan, E., Cai, Q., Dai, X., Zhussip, M., Kalyazin, N., Vyal, D., Zou, X., Yan, Y., Chung, H., Zhang, J., Yu, G., Zhang, F., Wang, H., Liao, B., Du, Z., Wu, Y.-L., Shi, G., Peng, L., Wang, Y., Cao, Y., Zha, Z., Huang, Z.-K., Chen, Y.-C., Chiang, Y.-C., Yang, H.-H., Chen, W.-T., Chang, H.-E., Chen, I.-H., Hsieh, C.-H., Kuo, S.-Y., Liu, X., Wang, Q., Pan, J., Yu, H., Yu, W., Ge, L., Dong, J., Zou, Y., Wu, Z., Han, B., Zhang, X., Zhang, H., Yin, X., Zuo, K., Deng, W., Yuan, H., Lu, Z., Ouyang, M., Ma, W., Liu, N., Zheng, H., Zhang, Y., Zhang, J., Chen, Z., Gendy, G., Sabor, N., Hou, J., He, G., Zhu, Y., Wang, X., Fu, X., Zha, Z.-J., Yin, D., Liu, M., Chen, B., Li, A., Luo, L., Jin, K., Zhu, C., Zhang, X., Xie, C., Li, L., Meng, H., Zhang, T., Li, T., Zhao, X., Zhang, Z., Li, B., Zheng, H., Zhao, S., Gao, Y., Ren, J., Hu, K., Shi, J., Wu, Z., Huang, D., Zhu, J., Li, H., Xv, Q., Liu, T., Weng, S., Wu, G., Jiang, J., Liu, X., Jiang, J., Zhang, M., Weng, S., Hu, J., Wu, C., Fan, Q., Feng, C., Luo, Z., Hu, S., Lyu, S., Wu, X., & Wang, X.
- Affiliations
- ETH Zurich (CH)
- ETH Zurich,Computer Vision Lab
- ORCID
- Li, Yawei
- Zhang, Yulun
- Timofte, Radu
- Li, Youwei
- Li, Xinpeng
- Jiang, Ting
- Wu, Qi
- Lin, Wenjie
- Jiang, Chengzhi
- Luo, Jinting
- Fan, Haoqiang
- Liu, Shuaicheng
- Wang, Yucong
- Cai, Minjie
- Li, Mingxi
- Zhang, Yuhang
- Sheng, Yankai
- Mao, Yanyu
- Sun, Long
- Pan, Jinshan
- Dong, Jiangxin
- Tang, Jinhui
- Wang, Yan
- Pan, Erlin
- Cai, Qixuan
- Dai, Xinan
- Zhussip, Magauiya
- Zou, Xueyi
- Zhang, Jin
- Zhang, Feng
- Wang, Hongbin
- Liao, Bohao
- Du, Zhibo
- Shi, Gege
- Peng, Long
- Wang, Yang
- Cao, Yang
- Chen, Yi-Chung
- Yang, Hao-Hsiang
- Kuo, Sy-Yen
- Liu, Xin
- Pan, Jiahao
- Yu, Hongyuan
- Yu, Weichen
- Dong, Jiahua
- Zou, Yajun
- Zhang, Xiaolin
- Zhang, Heng
- Deng, Weijian
- Yuan, Hongjie
- Liu, Nian
- Zheng, Hanyou
- Zhang, Yuantong
- Zhang, Junxi
- Chen, Zhenzhong
- Gendy, Garas
- Sabor, Nabil
- Hou, Jingchao
- He, Guanghui
- Zhu, Yurui
- Wang, Xi
- Fu, Xueyang
- Yin, Daheng
- Liu, Mengyang
- Li, Ao
- Luo, Lei
- Zhu, Ce
- Zhang, Xiaoming
- Xie, Chengxing
- Li, Linze
- Zhang, Tianlin
- Li, Tianrui
- Zhao, Xiaole
- Zhang, Zhao
- Li, Baiang
- Zheng, Huan
- Zhao, Suiyi
- Ren, Jiahuan
- Hu, Kang
- Wu, Zhijian
- Huang, Dingjiang
- Zhu, Jinchen
- Li, Hui
- Liu, Tianle
- Wu, Gang
- Jiang, Junpeng
- Liu, Xianming
- Jiang, Junjun
- Hu, Jing
- Wu, Chengxu
- Feng, Chengming
- Luo, Ziwei
- Hu, Shu
- Lyu, Siwei
- Wu, Xi
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 1922-1960
- OpenAlex
- W4385805159
- Cited by
- 72
- References
- 109
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 18 · 2023: 26 · 2022: 1
- FWCI
- 7.59
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- Funders
- Sony
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- single image super-resolution, efficient neural networks, execution time reduction, parameter reduction, FLOPs reduction, memory usage
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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