Learning for Computational Imaging (LCI) Workshop:
Sensing, Reconstruction, and Analysis
The ICCV Workshop on Learning for Computational Imaging (LCI) is a perfect venue for presenting recent advances and trends in the emerging field of computational imaging, in which learning and computation are the major ingredients of the highly effective imaging systems. Given the rapidly growing interest in next-generation imaging systems and their combination with computer vision tasks, LCI workshop welcomes recent work on novel imaging pipelines such as smart imaging system design, blind compressed sensing, and task-driven imaging systems. The scope covers research topics ranging from novel computational imaging pipeline, image and system modeling, theory, algorithms, applications in various imaging modalities, as well as industrial imaging applications.
Since data-driven and large-scale optimization methods are becoming more and more popular in the computational imaging community, a major focus of LCI will be on advanced models and machine learning techniques, including deep learning approaches, sparse and low-rank modeling, manifold learning, unrolled architectures, learning convolutional and tensor models, etc., which can be applied to enhance the effectiveness and efficiency of various imaging systems. Besides, LCI also promotes recent works on learning theory for computational imaging, including performance guarantee, convergence analysis, learning model analysis, etc., which are critical for reliable and interpretable imaging systems.
Last but not least, LCI will discuss research on the plethora of computational imaging applications, which include various imaging modalities (e.g., MRI, radar, lidar, microscopy, computational photography, optics, etc.). LCI will encourage close interaction between mathematical and applied computational imaging researchers and practitioners, and bring together experts in academia and industry working in imaging theory and applications, with focus on machine learning, data modeling, computer vision, signal processing, inverse problems, compressed sensing, optimization, neuroscience, learning and computation-driven hardware, and related areas.
- Novel Learning-Driven Computational Imaging Systems: Smart imaging systems with end-to-end learning, Learned data acquisition, Blind compressed sensing, Task-driven imaging system design, Optimal design of experiments, etc.
- Learning-based Modeling and Algorithms for Imaging: Deep learning approaches and architectures, Sparse and low-rank modeling, Dictionary and transform learning, Manifold learning, Unrolled architectures, Graphical models, Tensor models, Online learning, Plug-and-play models, Bayesian methods, etc.
- Learning Theory for Computational Imaging: Performance guarantees for learning-based methods, Convergence analysis of learning algorithms, Generative model recovery analysis, Analysis of deep architectures, Theory for large-scale and distributed algorithms, etc.
- Computational Imaging Applications: Magnetic resonance imaging, Radar imaging, Lidar, Computed tomography, Microscopy, Ultrasound, Hyperspectral imaging, Hybrid imaging, Computational photography, Neuroimaging, Dynamic imaging, Super-resolution, Inpainting, and novel and extreme imaging modalities and applications.
- Paper Submission Deadline: July 15, 2019 (11:59 PM PST)
- Author Notification Date: August 20, 2019 (11:59 PM PST)
- Camera-ready Deadline: August 30, 2019 (11:59 PM PST)
- Workshop Date: November 2, 2019 (Full-Day Event, Saturday)
This workshop is a full-day event, which will include invited talks, oral and poster presentation of accepted papers.
The workshop paper format (8 page limit) follows that of the ICCV 2019 main conference [link].
Please submit your papers to https://cmt3.research.microsoft.com/LCI2019
- Bihan Wen, Nanyang Technological University, Singapore ( email@example.com )
- Saiprasad Ravishankar, Michigan State University, USA ( firstname.lastname@example.org )
- Brendt Wohlberg, Los Alamos National Laboratory, USA ( email@example.com )
- Jong Chul Ye, Korea Advanced Institute of Science and Technology, Korea ( firstname.lastname@example.org )