基于多尺度局部结构化信息测度的鲁棒像素级图像融合算法

现有的图像融合算法不能有效区分基本图像特征和噪声，往往在输入存在噪声时性能变差。自然场景图像中，视觉上显著的图像特征往往表现出一定的几何结构，而噪声缺少结构化信息。基于复数小波变换，利用Von Neumann熵推导了一个结构化信息测度，可以有效区分噪声和视觉上显著的图像特征。图像融合之前利用测度加权输入，从而自适应抑制噪声同时保留图像特征。对几个图像融合方案从主、客观上进行性能比较，显示了新方案的优越性。

Multiscale Based Local Structurization Information Metric for Robust Pixel Level Image Fusion

Because previous methods can not identify underlying image features from noises effectively, the updated image fusion schemes will be degraded when inputs are corrupted with noise. The perceptual salient image features often manifest some geometric structures, while noise dominated images are less structured. Based on complex wavelet transform, a structurization information metric is formulated by means of the Von Neumann entropy. The formulated metric can distinguish image features from noise very well. During the fusion process, the metric is employed to weight all fusion inputs. As a result, the perceptual meaningful inputs are enhanced while the noise inputs are de-emphasized adaptively. Comparing several image fusion schemes subjectively and objectively shows the good performance of the new scheme.