A Nash game based variational model for joint image intensity correction and registration to deal with varying illumination

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Theljani, Anis and Chen, Ke (2020) A Nash game based variational model for joint image intensity correction and registration to deal with varying illumination. Inverse Problems, 36 (3). 034002. ISSN 0266-5611 (https://doi.org/10.1088/1361-6420/ab2934)

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Abstract

Registration aligns features of two related images so that information can be compared and/or fused in order to highlight differences and complement information. In real life images where bias field is present, this undesirable artefact causes inhomogeneity of image intensities and hence leads to failure or loss of accuracy of registration models based on minimization of the differences of the two image intensities. Here, we propose a non-linear variational model for joint image intensity correction (illumination and translation) and registration and reformulate it in a game framework. While a non-potential game offers flexible reformulation and can lead to better fitting errors, proving the solution existence for a non-convex model is non-trivial. Here we establish an existence result using the Schauder's fixed point theorem. To solve the model numerically, we use an alternating minimization algorithm in the discrete setting. Finally numerical results can show that the new model outperforms existing models.

ORCID iDs

Theljani, Anis and Chen, Ke ORCID logoORCID: https://orcid.org/0000-0002-6093-6623;
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