A novel fuzzy logic, gray wolf optimization and convolutional neural network based image fusion framework using discrete frame-let transform

In this paper, a novel fuzzy logic, gray wolf optimization and convolutional neural network (CNN) based infrared and visible image framework using discrete frame-let transform is proposed. A new enhancement method using fuzzy logic has been introduced to improve the contrast and visual appearance of input images. Discrete frame-let transform has been applied on both enhanced source images in order to generate more featured multi-scale coefficients. Gray wolf optimization (GWO) based fusion strategy has been introduced for merging low frequency coefficients of both enhanced source images in order to generate better target and as well as background details in the resultant fused image. Siamese convolutional neural network is applied on enhanced source images for integrating pixel activity information in order to obtain a weight map. High frequency coefficients are merged using through CNN generated weight map via discrete frame-let decomposition and local similarity based fusion strategy for generating more detailed information in the final fused image. Finally the reconstruction of resultant fused image is obtained through using inverse discrete frame-let transform. The obtained experimental results achieved through simulation clearly demonstrates that our proposed algorithm has shown superior performance in both aspects (i.e. subjective assessment and as well as objective assessment) compared to related state of art methods.