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 divides multi-scale coefficients only into large and small states, which can lead to an inaccurate statistical reduce quality of fusion result. Our method improves upon by developing soft context variable provide fine-grained representation high-frequency subbands, resulting improved results. Additionally, low-frequency subbands is performed on difference regional energy ensure visual quality. experimental results several datasets demonstrate that proposed outperforms other methods both subjective objective evaluations.