论文标题
基于多状态上下文隐藏马尔可夫模型的红外且可见的图像融合
Infrared and visible image fusion based on Multi-State Contextual Hidden Markov Model
论文作者
论文摘要
传统的两态隐藏马尔可夫模型仅将高频系数仅分为两个状态（大小状态）。这种方案很容易为高频子带产生不准确的统计模型，并降低了融合结果的质量。在本文中，建议在非缩放采样的剪切域中为红外且可见的图像融合提出了细粒度的多状态隐藏模型（MCHMM），该模型融合了，该模型融合了NSST系数的强相关性和详细信息水平。为此，从上下文相关性的角度相应地设计了准确的软上下文变量。然后，将MCHMM提供的统计特征用于高频子带融合。为了确保视觉质量，还为低频子带提出了基于区域能量差异的融合策略。实验结果表明，与主观和客观方面的其他融合方法相比，所提出的方法可以实现卓越的性能。
The traditional two-state hidden Markov model divides the high frequency coefficients only into two states (large and small states). Such scheme is prone to produce an inaccurate statistical model for the high frequency subband and reduces the quality of fusion result. In this paper, a fine-grained multi-state contextual hidden Markov model (MCHMM) is proposed for infrared and visible image fusion in the non-subsampled Shearlet domain, which takes full consideration of the strong correlations and level of details of NSST coefficients. To this end, an accurate soft context variable is designed correspondingly from the perspective of context correlation. Then, the statistical features provided by MCHMM are utilized for the fusion of high frequency subbands. To ensure the visual quality, a fusion strategy based on the difference in regional energy is proposed as well for lowfrequency subbands. Experimental results demonstrate that the proposed method can achieve a superior performance compared with other fusion methods in both subjective and objective aspects.
