Inverse Problems and Image Processing
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Organizer(s): |
Name:
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Affiliation:
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Country:
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Qiyu Jin
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Inner Mongolia University
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Peoples Rep of China
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Introduction:
| | Inverse problems are fundamental in applied mathematics and imaging sciences, playing a crucial role in medical imaging, remote sensing, and computational photography. This session aims to discuss recent advancements in the theory, numerical methods, and applications of inverse problems in image processing. Key topics include regularization techniques, variational models, optimization strategies, and machine learning-based approaches. Special attention will be given to structure-preserving methods that ensure stability, sparsity, and energy conservation. High-order numerical methods and hybrid model-driven and data-driven techniques are essential for solving complex inverse problems. This session will bring together researchers from various fields to present new developments, address challenges, and explore real-world applications such as MRI and CT reconstruction, blind deblurring, and super-resolution. The session aims to provide a platform for interdisciplinary collaboration and innovation in inverse problems and imaging. The topics will span a wide range from theoretical results to novel algorithms, and to a variety of interesting application areas. The workshop will provide a platform for applied mathematicians and application scientists to interact, communicate and foster collaborations.
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