๐Ÿ“š Biography


Jinfan Hu is pursuing his Ph.D. degree in XPixel Group, Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS). He is now supervised by Prof. Chao Dong. He obtained his Bachelor and Master degrees in Mathematics (supervised by Prof. Ting-Zhu Huang and Prof. Liang-Jian Deng) from the University of Electronic Science and Technology of China (UESTC), Chengdu, China in 2019 and 2022. His research interests include low-level vision (LV) and AI interpretability.

Email: jf.hu1@siat.ac.cn
Follow Me: Google Scholar โ€ƒ Github

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๐Ÿ”ฅ Headlines

  • [03/2025] ๐ŸŽ‰๐ŸŽ‰๐ŸŽ‰ One paper is accepted by TPAMI. We propose a model/task-agnostic interpreting method (CEM) for low-level vision models, bringing causality analysis into the field. We hope this work contributes to a deeper understanding and enhancement of low-level vision models!
  • [02/2025] ๐ŸŽ‰๐ŸŽ‰๐ŸŽ‰ We revisit the generalization problem of low-level vision models in this paper. We demonstrate that the common strategy of blindly expanding the training set is ineffective. Instead, the key to better generalization lies in guiding the network to learn the image content rather than the degradation!
  • [02/2025] ๐ŸŽ‰๐ŸŽ‰๐ŸŽ‰ The book ใ€Šๅบ•ๅฑ‚่ง†่ง‰ไน‹็พŽใ€‹, co-authored with my supervisor Prof. Chao Dong, has been published. We hope this book provides valuable insights for researchers and enthusiasts in the Low-level Vision field!
  • [01/2024] ๐ŸŽ‰๐ŸŽ‰๐ŸŽ‰ We release a groundbreaking image restoration method (SUPIR) that harnesses generative prior and the power of model scaling up. Our method demonstrates unprecedented performance in real-world image restoration tasks! Experience the future of image restoration through our official platforms: ๆ˜Ž็Š€AI and SupPixel AI.

๐Ÿ“ Publications

Book

Paper

  • J. Hu, J. Gu, S. Yao, F. Yu, Z. Li, Z. You, C. Lu, C. Dong. Interpreting Low-level Vision Models with Causal Effect Maps. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025. [Code][PDF]

  • J. Hu*, Z. You*, J. Gu, K. Zhu, T. Xue, C. Dong. Revisiting the Generalization Problem of Low-level Vision Models Through the Lens of Image Deraining. arXiv, 2025. [PDF]

  • F. Yu, J. Gu, J. Hu, Z. Li, and C. Dong. UniCon: Unidirectional Information Flow for Effective Control of Large-Scale Diffusion Models. International Conference on Learning Representations (ICLR), 2025. [PDF]

  • F. Yu, J. Gu, Z. Li, J. Hu, X. Kong, X. Wang, J. He, Y. Qiao, and C. Dong. Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild. Conference on Computer Vision and Pattern Recognition (CVPR), 2024. [Project Page][PDF]

  • R. Ran, L.-J. Deng, T.-X. Jiang, J.-F. Hu, J. Chanussot, and G. Vivone. GuidedNet: A General CNN Fusion Framework via High-resolution Guidance for Hyperspectral Image Super-resolution. IEEE Transactions on Cybernetics (TCYB), 2023. (ESI Highly Cited) [Code][PDF]

  • X. Liu*, J. Hu*, X. Chen, and C. Dong. UDC-UNet: Under-Display Camera Image Restoration via U-shape Dynamic Network. European Conference on Computer Vision Workshop (ECCVW), 2022. [Code][PDF]

  • Y.-W. Zhuo, T.-J. Zhang, J.-F. Hu, H.-X. Dou, T.-Z. Huang, and L.-J. Deng. Hyper-DSNet: Hyperspectral Pansharpening via A Deep-Shallow Fusion Network with Multi-Detail Extractor and Spectral Attention. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022. [PDF]

  • J.-F. Hu, T.-Z. Huang, L.-J. Deng, H.-X. Dou, D. Hong, and G. Vivone. Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution. IEEE Geoscience and Remote Sensing Letters, 2022. (ESI Highly Cited) [Code][PDF]

  • S. Peng, L.-J. Deng, J.-F. Hu, and Y.-W. Zhuo. Source-Adaptive Discriminative Kernels based Network for Remote Sensing Pansharpening. International Joint Conferences on Artificial Intelligence (IJCAI), 2022. [Code][PDF]

  • J.-F. Hu, T.-Z. Huang, L.-J. Deng, T.-X. Jiang, G. Vivone, and J. Chanussot. Hyperspectral Image Super-Resolution via Deep Spatiospectral Attention Convolutional Neural Networks. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. (ESI Highly Cited) [Project Page][PDF]

  • T. Xu, T.-Z. Huang, L.-J. Deng, X.-L Zhao, and J.-F. Hu. Exemplar-based Image Inpainting Using Adaptive Two-Stage Structure-Tensor Based Priority Function and Nonlocal Filtering. Journal of Visual Communication and Image Representation, 2021. [PDF]

  • Z.-C. Wu, T.-Z. Huang, L.-J. Deng, J.-F. Hu, and G. Vivone. VO+ Net: An Adaptive Approach Using Variational Optimization and Deep Learning for Panchromatic Sharpening. IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021. [Project page][PDF]

  • Z.-C. Wu, T.-Z. Huang, L.-J. Deng, G. Vivone, J.-Q Miao, J.-F. Hu, and X.-L Zhao. A New Variational Approach Based on Proximal Deep Injection and Gradient Intensity Similarity for Spatio-Spectral Image Fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020. [Project page][PDF]

(* Equal contributions)

๐Ÿ“ธ Products

For a comprehensive exploration of our technologies, visit our official websites: ๆ˜Ž็Š€AI and SupPixel AI.

๐Ÿ“– Experiences

  • 09/2022-present: Ph.D. student in computer science. (Supervisor: Prof. Chao Dong); Shenzhen Institute of Advanced Technology (SIAT), University of Chinese Academy of Sciences (UCAS)

  • 09/2019-06/2022: Master student in mathematics. (Supervisor: Prof. Ting-Zhu Huang and Prof. Liang-Jian Deng); University of Electronic Science and Technology of China (UESTC)

  • 09/2015-06/2019: Bachelor student in information and computing science; University of Electronic Science and Technology of China (UESTC)

๐Ÿ† Honors and Awards

  • 2nd Place in ECCV MIPI 2022 Challenge on UDC image restoration, 2022

  • National Scholarship, 2021

  • National First Prize of CUMCM, 2017

๐Ÿ’ฌ Academic Activities

Peer-Reviewer:

  • IEEE Transactions on Pattern Analysis and Machine Intelligence

  • IEEE Transactions on Cybernetics

  • IEEE Transactions on Geoscience And Remote Sensing

  • IEEE Geoscience and Remote Sensing Letters

  • IEEE Transactions on Computational Imaging
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