基于NSST 域隐马尔可夫树模型的SAR 和灰度可见光图像融合

针对合成孔径雷达(SAR) 图像和可见光图像融合问题, 提出一种基于非下采样剪切波变换域的隐马尔可夫树模型的图像融合方法(NHMM), 图像经过非下采样剪切波变换(NSST) 分解形成一个低频子带和多个高频子带.在NSST 域中, 对低频系数采用基于标准差的融合策略; 针对高频子带, 建立NSST 域隐马尔可夫树(HMT) 模型对高频系数进行训练, 并根据梯度能量对训练后的高频系数进行选择, 最后通过NSST 逆变换得到融合图像. 实验结果表明, 所提出的方法可提高图像的融合质量, 并能降低图像噪声, 具有一定的有效性和实用性.

Fusion technique for SAR and gray visible image based on hidden Markov model in non-subsample shearlet transform domain

To exact more directional information and important detail information from the images effectively, an image fusion algorithm for synthetic aperture radar(SAR) and grayscale visible light images based on the hidden Markov model(HMM) in the non-subsample Shearlet transform(NSST) domain is proposed. In NSST domain, the low frequency factors are fused by standard deviation. Meanwhile, the hidden Markov tree(HMT) model is built to train the high frequency factors. Then the energy gradient is used to select the trained high frequency factors. Thus, the low frequency and high frequency images are fused by inverse transformation of NSST to get the final image. Finally, the simulation results show that, compared with other multi-scale HMT models and traditional NSST fusion strategy, the proposed method can promote the fusion quality and enhance the information of the images, while reducing noise as well, and also show its effectiveness and feasibility.