Bayesian image segmentation under varying blur with triplet Markov random field

Sonia Ouali (1) , Jean-Baptiste Courbot (1) , Romain Pierron (2) , Olivier Haeberlé (1)

1 IRIMAS - Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499
2 LVBE - Laboratoire Vigne Biotechnologie et Environnement

In this paper, we place ourselves in the context of the Bayesian framework for image segmentation in the presence of varying blur. The proposed approach is based on Triplet Markov Random Fields (TMRF). This method takes into account, during segmentation, peculiarities of an image such as noise, blur, and texture. We present an unsupervised TMRF method, which jointly deals with the problem of segmentation, and that of depth estimation in order to process fluorescence microscopy images. In addition to the estimation of the depth maps using the Metropolis-Hasting and the Stochastic Parameter Estimation (SPE) algorithms, we also estimate the model parameters using the SPE algorithm. We compare our TMRF method to other MRF models on simulated images, and to an unsupervised method from the state of art on real fluorescence microscopy images. Our method offers improved results, especially when blur is important.

Mots clés: Markov random fields; image segmentation; deconvolution; fluorescence microscopy

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

Sonia Ouali, Jean-Baptiste Courbot, Romain Pierron, Olivier Haeberlé. Bayesian image segmentation under varying blur with triplet Markov random field. Inverse Problems, In press. ⟨hal-04660805⟩