Image Reconstruction

Image reconstruction is a fundamental inverse problem arising across diverse scientific and engineering domains — from medical imaging and remote sensing to computational photography. Given incomplete, noisy, or indirectly observed measurements, the goal is to recover the underlying image faithfully. We develop robust variational models and efficient algorithms to address challenging reconstruction problems, with an emphasis on theoretical guarantees and practical performance.

Tomographic Reconstruction

Tomographic reconstruction aims at recovering the internal structure of an object from a collection of projected measurements taken at different angles. The problem is inherently ill-posed. We develop variational and deep learning models that incorporate prior knowledge of the underlying structure to achieve high-quality reconstruction from limited and possibly dynamical data.

References

(He et al., 2023) (Zhang et al., 2025) (Wu et al., 2026)