Many image reconstruction tasks amount to solve ill-posed inverse problems.Indeed,measurement devices typically cannot record all the information needed to recover the sought-after object; furthermore,the operators that model these devices are seldom accurate and data are corrupted by various perturbations.A common approach to find an approximate to the unknown object is regularization.The key points are the correct choices of the data fidelity term and the regularization term,as well as the trade-off between these terms.This is a challenging problem since the optimal solutions of the whole functional should correctly reflect the knowledge on the data-production process and the priors on the unknown object.The optimal solutions usually cannot be computed explicitly and iterative schemes are used.This symposium focus on imaging inverse problemsmathematical models,numerical algorithms,theoretical analysis and various applications,especially,applied to CT reconstruction and some processing techniques for images.