We work on developing novel and practical techniques to address a range of image and video restoration problems, including image super-resolution, denoising, video super-resolution, details enhancement, video deblurring, frame interpolation, low-light image enhancement, and also lately for NeRF-synthesized view enhancement.
Some of our recent work:
K. Zhou, X. Lin, and J. Lu, “TSP-Mamba: The Travelling Salesman Problem Meets Mamba for Image Super-resolution and Beyond,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), Nashville, TN, Jun. 2025. (PDF)
K. Zhou, X. Lin, Z. Liu, X. Han, and J. Lu, “UPS: Unified Projection Sharing for Lightweight Single-Image Super-resolution and Beyond,” Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec. 2024. (PDF)
K. Zhou, X. Lin, W. Li, X. Xu, Y. Cai, Z. Liu, X. Han, and J. Lu, “Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement ,” European Conf. Computer Vision (ECCV), Milano, Italy, Oct. 2024. (Project page) (PDF)
J. Chen, Y. Qin, L. Liu, J. Lu, and G. Li, “NeRF-HuGS: Improved NeRF in Non-static Scenes Using Heuristics-Guided Segmentation,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), Seattle, WA, Jun. 2024. (Oral & also Best Paper Award candidate, top 0.2% of 11, 532 paper submissions) (Project page)
H. Liu, Y. Qian, Y. Liang, B. Zhang, Z. Liu, T. He, W. Zhao, J. Lu, and B. Yu, “A High-Performance Accelerator for Real-Time Super-Resolution on Edge FPGAs,” ACM Trans. on Design Automation of Electronic Systems (TODAES), 2024. (preprint)
K. Zhou, W. Li, N.-J. Jiang, X. Han, and J. Lu, “From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm,” IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), to appear 2023. (preprint) (Project page)
W. Zhao, Y. Bai, Q. Sun, W. Li, H. Zheng, N. Jiang, J. Lu, B. Yu, and M. D.-F. Wong, “A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU,” IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2023. (link)
W. Li, X. Lu, S. Qian, and J. Lu, “On Efficient Transformer-Based Image Pre-training for Low-Level Vision,” in Proc. International Joint Conference on Artificial Intelligence (IJCAI) , Macao, S. A. R., Aug. 2023. (PDF)
K. Zhou, W. Li, Y. Wang, T. Hu, N. Jiang, X. Han, and J. Lu, “NeRFLiX: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXer,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, Jun. 2023. (PDF) (Project page)
K. Zhou, W. Li, L. Lu, X. Han, and J. Lu, “Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, Jun. 2023. (PDF)
X. Xu, R. Wang, and J. Lu, “Low-Light Image Enhancement via Structure Modeling and Guidance,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, Jun. 2023. (PDF)
H. Liu, L. Li, J. Lu, and S. Tan, “Group Sparsity Mixture Model and Its Application on Image Denoising,” IEEE Trans. on Image Processing (TIP), vol. 31, pp. 5677-5690, 2022. (link)
K. Zhou, W. Li, L. Lu, X. Han, and J. Lu, “Revisiting Temporal Alignment for Video Restoration,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, Jun. 2022. (PDF)
L. Lu, R. Wu, H. Lin, J. Lu, and J. Jia, “Video Frame Interpolation with Transformer,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, Jun. 2022. (PDF)
W. Li, K. Zhou, L. Qi, L. Lu, and J. Lu, “Best-Buddy GANs for Highly Detailed Image Super-Resolution,” AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, Feb. 2022. (Oral presentation) (PDF) (Slides)
H. Liu, X. Liu, J. Lu, and S. Tan, “Self-Supervised Image Prior Learning with GMM from a Single Noisy Image,” in Proc. IEEE Int. Conf. Computer Vision (ICCV), Oct. 2021. (Oral presentation, A.R.= 3.4%) (PDF) (Slides)
R. Wang, X. Xu, C.-W. Fu, J. Lu, B. Yu, and J. Jia, “Seeing Dynamic Scenes in the Dark: A High-Quality Video Dataset with Mechatronic Alignment,” in Proc. IEEE Int. Conf. Computer Vision (ICCV), Oct. 2021. (PDF) (Project page)
L. Lu, W. Li, X. Tao, J. Lu, and J. Jia, “MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-resolution,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), Nashville, TN, Jun. 2021. (PDF)
W. Li, K. Zhou, L. Qi, N. Jiang, J. Lu, and J. Jia, “LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond,” Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec. 2020. (PDF) (Supplementary) (Code)
W. Li, X. Tao, T. Guo, L. Qi, J. Lu, and J. Jia, “MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution,” European Conf. Computer Vision (ECCV), Glasgow, UK, Aug. 2020. (PDF) (Code)