基于多分辨率局部结构化信息熵的鲁棒多模图像融合算法
Multiresolution Based Local Structured Information Entropy for Robust Multimodal Image Fusion
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摘要: 目前的图像融合算法不能区分噪声和视觉上有意义的图像特征,往往将噪声当作有意义的信息传输到融合结果中。针对这一问题,该文基于复数小波变换(CWT),将图像的结构化特征表现在不同尺度和方向上,定义了两种结构化信息熵,表达局部图像结构化程度:带内结构化信息熵,以及考虑带间特征相关性的结构化信息熵。利用定义的两种测度,在图像融合之前对输入加权处理,使视觉上有意义的信息在融合结果中自适应地增强,而噪声自适应地抑制。通过对融合算法仿真结果的主观比较和客观性能分析,展示了本文提出的图像融合算法的优越性。Abstract: The updated image fusion schemes could not identify meaningful image features from noises, the input noise is treated as valid information and transferred into the fused output. After complex wavelet transformation (CWT), structured information is decomposed into varying scales and directions. Based on CWT, two structured information entropies, intra-band structured information entropy and inter-band structured information entropy, are formulated to express the structurization level of image features. Preceding the image fusion process, the metrics are employed to weight all inputs. As a result, the perceptual salient inputs are enhanced while the noise inputs are de-emphasized adaptively. Comparing the visual aesthetics of fusion results and analyzing the performance objectively, show the good performance of the proposed image fusion algorithm.
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