Infrared and Visible Image Fusion Based on Illumination Perception and Dense Net
Aiming at the problems of ignoring illumination imbalance,low contrast and texture detail loss in existing meth-ods,this paper proposed a fusion method of infrared and visible image based on illumination perception and dense network.Firstly,the illumination probability was obtained from the visible image and the illumination perception weight was calcu-lated to guide the training network.The adaptive information retention of the source image was calculated by the feature ex-traction and information measurement module to maintain the adaptive similarity between the fusion result and the source image.At the same time,the illumination perception loss and the similarity constraint loss function enabled the model to generate all-weather fusion images containing significant objects and rich texture details in terms of structure,contrast and brightness.In this study,two public data sets,TNO and MSRS,were used for subjective and objective assessment.The ex-perimental results show that this study can make up for the defect of illumination imbalance,and effectively retain more tex-ture details of visible images while preserving more infrared targets.
image fusioninfrared imagevisible imageillumination perceptiondense net
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