Multiresolution Based Local Structured Information Entropy for Robust Multimodal Image Fusion
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

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.