| Multi-scale residual network for image super-resolution J Li, F Fang, K Mei, G Zhang Proceedings of the European conference on computer vision (ECCV), 517-532, 2018 | 964 | 2018 |
| Ntire 2018 challenge on image dehazing: Methods and results C Ancuti, CO Ancuti, R Timofte Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 183 | 2018 |
| Progressive feature fusion network for realistic image dehazing K Mei, A Jiang, J Li, M Wang Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth …, 2019 | 148 | 2019 |
| MDCN: Multi-scale dense cross network for image super-resolution J Li, F Fang, J Li, K Mei, G Zhang IEEE Transactions on Circuits and Systems for Video Technology 31 (7), 2547-2561, 2020 | 100 | 2020 |
| VIDM: Video Implicit Diffusion Models K Mei, VM Patel AAAI Conference on Artificial Intelligence (AAAI), 2023 | 92 | 2023 |
| AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing Q Song, K Mei, R Huang Proceedings of the AAAI Conference on Artificial Intelligence, 2021 | 81 | 2021 |
| AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models NG Nair, K Mei, VM Patel IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022 | 66 | 2022 |
| Aim 2019 challenge on raw to rgb mapping: Methods and results A Ignatov, R Timofte, SJ Ko, SW Kim, KH Uhm, SW Ji, SJ Cho, JP Hong, ... 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW …, 2019 | 41* | 2019 |
| Lightweight and accurate recursive fractal network for image super-resolution J Li, Y Yuan, K Mei, F Fang Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 31 | 2019 |
| Higher-resolution network for image demosaicing and enhancing K Mei, J Li, J Zhang, H Wu, J Li, R Huang IEEE/CVF International Conference on Computer Vision, 3441-3448, 2019 | 29* | 2019 |
| Ltt-gan: Looking through turbulence by inverting gans K Mei, VM Patel IEEE Journal of Selected Topics in Signal Processing 17 (3), 587-598, 2023 | 22 | 2023 |
| Latent Feature-Guided Diffusion Models for Shadow Removal K Mei, L Figueroa, Z Lin, Z Ding, S Cohen, VM Patel IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 4313-4322, 2024 | 17 | 2024 |
| Scale-aware network with regional and semantic attentions for crowd counting under cluttered background Q Yi, Y Liu, A Jiang, J Li, K Mei, M Wang arXiv preprint arXiv:2101.01479, 2021 | 14 | 2021 |
| An effective single-image super-resolution model using squeeze-and-excitation networks K Mei, A Jiang, J Li, J Ye, M Wang Neural Information Processing: 25th International Conference, ICONIP 2018 …, 2018 | 14 | 2018 |
| CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation K Mei, M Delbracio, H Talebi, Z Tu, VM Patel, P Milanfar Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 11* | 2024 |
| Deep residual refining based pseudo‐multi‐frame network for effective single image super‐resolution K Mei, A Jiang, J Li, B Liu, J Ye, M Wang IET Image Processing 13 (4), 591-599, 2019 | 8 | 2019 |
| Bigger is not always better: Scaling properties of latent diffusion models K Mei, Z Tu, M Delbracio, H Talebi, VM Patel, P Milanfar arXiv preprint arXiv:2404.01367 3, 2024 | 7 | 2024 |
| Deep semantic statistics matching (D2SM) denoising network K Mei, VM Patel, R Huang European Conference on Computer Vision, 384-400, 2022 | 7 | 2022 |
| A comparison of different atmospheric turbulence simulation methods for image restoration NG Nair, K Mei, VM Patel 2022 IEEE International Conference on Image Processing (ICIP), 3386-3390, 2022 | 7 | 2022 |
| Bi-noising diffusion: Towards conditional diffusion models with generative restoration priors K Mei, NG Nair, VM Patel arXiv preprint arXiv:2212.07352, 2022 | 6 | 2022 |
Juncheng LiShanghai UniversityEmail verificata su shu.edu.cn