基于多状态上下文隐马尔可夫模型的红外与可见光图像融合
Infrared and visible image fusion based on Multi-State Contextual Hidden Markov Model
摘要：
传统的两状态隐马尔可夫模型只将高频系数分为两个状态（大状态和小状态）。这种方法容易对高频子带产生不准确的统计模型，降低融合结果的质量。针对非下采样Shearlet域红外与可见光图像融合问题，提出了一种细粒度多状态上下文隐马尔可夫模型（MCHMM），充分考虑了NSST系数的强相关性和细节层次性.为此，从上下文关联的角度设计了一个准确的软上下文变量。然后，利用MCHMM提供的统计特征进行高频子带融合。为了保证图像的视觉质量，提出了一种基于区域能量差异的低频子带融合策略。实验结果表明，该方法在主观和客观方面都优于其他融合方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
