A New Unsupervised Infrared and Visible Image Fusion Method Based on Salient Object Segmentation under Poor Illumination

Abstract

This work aims to propose a new unsupervised infrared and visible image fusion method based on salient object segmentation, which can obtain a fused image with more information on salient object and realize the salient object segmentation under poor illumination.The new method can be divided into four steps: (1) A new superpixel segmentation method based on simple linear iterative clustering (SLIC) with K-means subdivision is used to initially process the infrared and visible image, which has better superpixel segmentation quality. (2) A new improved Density Peaks Clustering (DPC) based on superpixel is used to realize the salient object segmentation of the infrared image, which is improved to be automatically selecting the cluster centers with less computation cost. (3) A new GrabCut strategy using the eroded and dilated salient object regions of the infrared image to predetermine the foreground and background respectively is used to achieve the salient object segmentation of the visible image, which can be totally automatic with better salient object segmentation quality. (4) An image fusion strategy is used to realize the final image fusion, which treats the salient object region and background respectively.Experiments were carried out under different poor illumination scenes in the real world. The experimental results show that the new infrared and visible image fusion method is successful with Q<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AB/F</sup> greater than 0.69. In addition, the provided superpixel segmentation method, salient object segmentation method and new GrabCut strategy are also effective. The research results provide an effective infrared and visible image fusion thought and three useful methods, which can provide a good reference for researchers. And, the research work reveals the application potential of DPC on image fusion and salient object segmentation, and broadens the application fields of DPC.