Infrared and visible image fusion based on Multi-State contextual hidden Markov Model

In this paper, we propose a novel multi-state contextual hidden Markov model (MCHMM) in the non-subsampled Shearlet transform (NSST) domain for image fusion. The traditional two-state hidden Markov model divides the multi-scale coefficients only into large and small states, which can lead to an inac-curate statistical model and reduce the quality of the fusion result. Our method improves upon this by developing a multi-state model and a soft context variable to provide a fine-grained representation of the high-frequency subbands, resulting in improved fusion results. Additionally, the fusion of low-frequency subbands is performed on the difference of regional energy to ensure visual quality. Our experimental results on several datasets demonstrate that the proposed method outperforms other fusion methods in both subjective and objective evaluations.(c) 2023 Elsevier Ltd. All rights reserved.

Automated summary

In this paper, a novel multi-state contextual hidden Markov model (MCHMM) in the non-subsampled Shearlet transform (NSST) domain is proposed for image fusion. The proposed method improves upon the traditional two-state hidden Markov model by developing a multi-state model and a soft context variable, resulting in improved fusion results. The experimental results on several datasets demonstrate that the proposed method outperforms other fusion methods in both subjective and objective evaluations.