Inverse Poblems and Image Processing

Research at our chair

In Progress

General references

• Classical sparse coding algorithms and their mathematical foundations for image processing
  Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing (https://link.springer.com/book/10.1007/978-1-4419-7011-4)
• A survey on theoretical developments of deep learning in inverse problems
• Theoretical Perspectives on Deep Learning Methods in Inverse Problems (https://ieeexplore.ieee.org/document/10035380)
• In addition to sparsity and inverse problems, the review paper provides an up-to-date introduction on improving optimization methods via machine learning, including plug-and-play and algorithm unrolling
  Learning to Optimize: A Primer and A Benchmark (https://jmlr.org/papers/v23/21-0308.html)
• Analysis on the relationship between deep learning and inverse problems
  i) Convolutional Neural Networks Analyzed via Convolutional Sparse Coding (https://arxiv.org/abs/1607.08194)
  ii) Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds (https://arxiv.org/abs/1808.10038)
• Important deep learning model, U-Net and visual transformer, for computer vision
  i) U-Net: Convolutional Networks for Biomedical Image Segmentation (https://arxiv.org/abs/1505.04597)
  ii) A Survey on Vision Transformer (https://arxiv.org/abs/2012.12556)

Contact

Do you have questions about our research in this area?

Please do not hesitate to contact us directly. Please send an e-mail to Jianfei Li, Postdoc in the field of Inverse Problems and Image Processing.

Inquiries from students are very welcome!