基于MSPCNN与FCM的红外与可见光图像融合

针对红外和可见光图像融合存在的轮廓信息不全、边缘及纹理细节信息缺失等问题，提出一种改进简化脉冲耦合神经网络（Improved Simplified Pulse Coupled Neural Network, MSPCNN）和模糊C-均值（Fuzzy C-mean, FCM）图像融合算法。首先，将红外和可见光图像用非下采样剪切波算法（NonSubsampled Shearlet Transform,NSST）分解为高低频子带；然后对分解后的高频子带采用MSPCNN融合，用一种高斯分布权重矩阵进行处理，增强细节信息和对比度；接着，将得到的低频子带图像使用FCM聚类算法进行聚类中心提取，设置聚类中心近似阈值简化过程，实现背景分类提取；最后利NSST进行逆变换，从而完成红外和可见光的图像融合过程。通过客观评价指标计算，本文所提方法在平均梯度、标准差、平均相似度等参考指标上相对于其他同类型算法均有改善提高，由于模型参数的简化，算法运行速度相对于其他算法得到提升，算法更适用于复杂场景。

Infrared and Visible Image Fusion Based on MSPCNN and FCM

Aiming at the problems of incomplete contour information, missing edge and texture details in infrared and visible image fusion, A Improved Simplified Pulse Coupled Neural Network (MSPCNN) and Fuzzy C-mean (FCM) image fusion algorithm is proposed. First, infrared and visible images were decomposed into high and low frequency sub-bands using the Non-Subsampled Shearlet Transform (NSST).Then MSPCNN is used to fuse the decomposed high frequency subband, and a Gaussian distribution weight matrix is used for processing to enhance the detail information and contrast. Then, the obtained low-frequency sub-band images were extracted by using FCM clustering algorithm, and the approximate threshold of clustering center was set to simplify the process to achieve background classification extraction.Finally, the inverse transformation of NSST is carried out to complete the infrared and visible image fusion process.Through objective evaluation index calculation, compared with other algorithms of the same type, the method proposed in this paper has been improved in terms of average gradient, standard deviation, average similarity and other reference indexes. As the running speed of simplified algorithm of model parameters has been improved, the timeliness of the algorithm in this paper has been improved compared with other algorithms, and the algorithm is more suitable for complex scenarios.