Infrared and visible image fusion based on GEMD and improved PCNN

Because of the limitations of decomposition tools in traditional image fusion methods, artifacts, decreased brightness, and contrast appear on fused image edges. A gradient edge-preserving multi-level decomposition(GEMD)-based approach for infrared and visible image fusion, as well as an enhanced pulse-coupled neural network(PCNN), are described. The gradient bilateral filter(GBF) is proposed based on the bilateral filter and the gradient filter(GF), which can preserve the edge structure, brightness, and contrast information while smoothing the detail information. Firstly, the source images are divided into three layers of feature maps and a base layer, and then a multi-level decomposition model is built using a gradient bilateral filter and gradient filter. Each layer of feature maps has two different structures, thin and thick. Then, according to the characteristics of the information contained in each feature map, the PCNN, which introduces an improved Laplacian operator in the input stimulus to enhance weak details captured in the image, regional energy, and contrast saliency are respectively adopted to obtain the fusion images of each sub-feature and the fusion image of the base layer as the fusion rules. Finally, the sub-fusion images are superimposed to obtain the final fusion image. Through experimental verification, the proposed method has improved both visual effect and quantitative evaluation and improved the brightness and contrast information of infrared and visible fusion images. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.