A block parallel majorize-minimize memory gradient algorithm

Sara Cadoni (1) , Emilie Chouzenoux (2, 1) , Jean-Christophe Pesquet (1, 2) , Caroline Chaux (3)

1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 GALEN - Organ Modeling through Extraction, Representation and Understanding of Medical Image Content
3 I2M - Institut de Mathématiques de Marseille

In the field of 3D image recovery, huge amounts of data need to be processed. Parallel optimization methods are then of main interest since they allow to overcome memory limitation issues, while benefiting from the intrinsic acceleration provided by recent multicore computing architectures. In this context, we propose a Block Parallel Majorize-Minimize Memory Gradient (BP3MG) algorithm for solving large scale optimization problems. This algorithm combines a block coordinate strategy with an efficient parallel update. The proposed method is applied to a 3D microscopy image restoration problem involving a depth-variant blur, where it is shown to lead to significant computational time savings with respect to a sequential approach.

Mots clés: Majorization-Minimization; Block- alternating optimization; Parallel algorithm; Memory gradient technique; 3D deconvolution; Depth-varying blur

Domaines: Traitement du signal et de l'image [eess.SP]

Sara Cadoni, Emilie Chouzenoux, Jean-Christophe Pesquet, Caroline Chaux. A block parallel majorize-minimize memory gradient algorithm. IEEE International Conference on Image Processing (ICIP 2016), Sep 2016, Phoenix, AZ, United States. pp.3194 - 3198, ⟨10.1109/ICIP.2016.7532949⟩. ⟨hal-01418417⟩