| A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network D Li, R Wang, C Xie, L Liu, J Zhang, R Li, F Wang, M Zhou, W Liu Sensors 20 (3), 578, 2020 | 245 | 2020 |
| Deep fourier-based exposure correction network with spatial-frequency interaction J Huang, Y Liu, F Zhao, K Yan, J Zhang, Y Huang, M Zhou, Z Xiong European Conference on Computer Vision, 163-180, 2022 | 132 | 2022 |
| Embedding fourier for ultra-high-definition low-light image enhancement C Li, CL Guo, M Zhou, Z Liang, S Zhou, R Feng, CC Loy arXiv preprint arXiv:2302.11831, 2023 | 117 | 2023 |
| Frequency and spatial dual guidance for image dehazing H Yu, N Zheng, M Zhou, J Huang, Z Xiao, F Zhao European Conference on Computer Vision, 181-198, 2022 | 107 | 2022 |
| An effective data augmentation strategy for CNN-based pest localization and recognition in the field R Li, R Wang, J Zhang, C Xie, L Liu, F Wang, H Chen, T Chen, H Hu, X Jia, ... IEEE access 7, 160274-160283, 2019 | 103 | 2019 |
| Effective pan-sharpening by multiscale invertible neural network and heterogeneous task distilling M Zhou, J Huang, X Fu, F Zhao, D Hong IEEE Transactions on Geoscience and Remote Sensing 60, 1-14, 2022 | 101 | 2022 |
| Mutual information-driven pan-sharpening M Zhou, K Yan, J Huang, Z Yang, X Fu, F Zhao Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 95 | 2022 |
| Agripest: A large-scale domain-specific benchmark dataset for practical agricultural pest detection in the wild R Wang, L Liu, C Xie, P Yang, R Li, M Zhou Sensors 21 (5), 1601, 2021 | 91 | 2021 |
| Spatial-frequency domain information integration for pan-sharpening M Zhou, J Huang, K Yan, H Yu, X Fu, A Liu, X Wei, F Zhao European conference on computer vision, 274-291, 2022 | 87 | 2022 |
| Pan-sharpening with customized transformer and invertible neural network M Zhou, J Huang, Y Fang, X Fu, A Liu Proceedings of the AAAI conference on artificial intelligence 36 (3), 3553-3561, 2022 | 81 | 2022 |
| Ingredient-oriented multi-degradation learning for image restoration J Zhang, J Huang, M Yao, Z Yang, H Yu, M Zhou, F Zhao Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 70 | 2023 |
| Fourmer: An efficient global modeling paradigm for image restoration M Zhou, J Huang, CL Guo, C Li International conference on machine learning, 42589-42601, 2023 | 65 | 2023 |
| Image de-raining via continual learning M Zhou, J Xiao, Y Chang, X Fu, A Liu, J Pan, ZJ Zha Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 65 | 2021 |
| Memory-augmented deep unfolding network for guided image super-resolution M Zhou, K Yan, J Pan, W Ren, Q Xie, X Cao International Journal of Computer Vision 131 (1), 215-242, 2023 | 61 | 2023 |
| Pan-mamba: Effective pan-sharpening with state space model X He, K Cao, J Zhang, K Yan, Y Wang, R Li, C Xie, D Hong, M Zhou Information Fusion 115, 102779, 2025 | 60 | 2025 |
| Memory-augmented deep conditional unfolding network for pan-sharpening G Yang, M Zhou, K Yan, A Liu, X Fu, F Wang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 57 | 2022 |
| Exposure normalization and compensation for multiple-exposure correction J Huang, Y Liu, X Fu, M Zhou, Y Wang, F Zhao, Z Xiong Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 55 | 2022 |
| HCLR-net: Hybrid contrastive learning regularization with locally randomized perturbation for underwater image enhancement J Zhou, J Sun, C Li, Q Jiang, M Zhou, KM Lam, W Zhang, X Fu International Journal of Computer Vision, 1-25, 2024 | 51 | 2024 |
| Deep fourier up-sampling M Zhou, H Yu, J Huang, F Zhao, J Gu, CC Loy, D Meng, C Li Advances in Neural Information Processing Systems 35, 22995-23008, 2022 | 51* | 2022 |
| Learning sample relationship for exposure correction J Huang, F Zhao, M Zhou, J Xiao, N Zheng, K Zheng, Z Xiong Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 45 | 2023 |