Primal-dual interior-point optimization based on majorization-minimization for edge-preserving spectral unmixing
Primal-dual interior-point methods are used in image processing to solve inversion problems that can be reduced to constrained convex minimization. Such iterative methods require the solution of a sequence of linear systems which are used to derive descent directions. This approach is very consuming in terms of computing time and memory usage for large-scale problems, unless the involved matrices have a specific structure. This is the case in the spectral unmixing problem where these matrices are block-diagonal when no spatial regularization is considered. Here, we consider the edge-preserving regularized case and we propose to tackle the linear system solving using a majorization-minimization (MM) approach based on separable quadratic majorant functions. The resulting systems have the same structure as in the non-regularized case and can thereby be solved efficiently. The interior-point algorithm is speeded-up while remaining convergent. An example of spectral unmixing is proposed to illustrate the efficiency of this approach.