Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin, China.
Department of Electrical Engineering, Northeast Electric Power University, Jilin, China.
PLoS One. 2020 Sep 18;15(9):e0239535. doi: 10.1371/journal.pone.0239535. eCollection 2020.
To solve the problem that the details of fusion images are not retained well and the information of feature targets is incomplete, we proposed a new fusion method of infrared (IR) and visible (VI) image-IR and VI image fusion method of dual non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN). The method makes full use of the flexible multi-resolution and multi-directional of NSCT, and the global coupling and pulse synchronization excitation characteristics of PCNN, effectively combining the features of IR image with the texture details of VI image. Experimental results show that the algorithm can combine IR and VI image features well. At the same time, the obtained fusion image can better display the texture information of image. The fusion performance in contrast, detail information and other aspects is better than the classical fusion algorithm, which has better visual effect and evaluation index.
为了解决融合图像的细节保留不好且特征目标的信息不完整的问题,提出了一种新的红外(IR)和可见(VI)图像融合方法,即基于双非抽样轮廓波变换(NSCT)和脉冲耦合神经网络(PCNN)的 IR 和 VI 图像融合方法。该方法充分利用 NSCT 的灵活多分辨率和多方向性,以及 PCNN 的全局耦合和脉冲同步激励特性,有效地将 IR 图像的特征与 VI 图像的纹理细节相结合。实验结果表明,该算法能够很好地结合 IR 和 VI 图像的特征。同时,得到的融合图像能够更好地显示图像的纹理信息。在对比度、细节信息等方面的融合性能优于经典融合算法,具有更好的视觉效果和评价指标。