In the past few years, a convolutional neural network (CNN) based deep learning model has been broadly applied in image processing and computer vision. And different from other multiscale decomposition methods in infrared and visible image fusion field, a hybrid l0-l1 layer decomposition model, which combines the superiority of l0 sparsity term and l1 sparsity term, is carried out to decompose the image into the base layer and the detail layer. Thus, a CNN model and visual saliency-based methods are utilized to fuse the detail layer and the base layer, respectively. Finally, the experiments show that this combination of CNN and saliency detection fusion rule has outperform some of the existing methods in infrared and visible image fusion both subjective and objective evaluations.
As an important branch of multi-source image fusion, infrared and visible image fusion not only inherits the basic theory and method of image fusion, but also has its own characteristics. Visual saliency detection method reflects the significant information in infrared image, and the feature has the consistent in the object information and background details with the infrared and visible light images. So, this paper proposed a novel framework of infrared and visible image fusion by using visual saliency and non-subsampled shearlet transformation. Comparing the proposed fusion method with some existing algorithms, the experimental results show that the proposed method can not only highlight the object information, but also can preserve the abundant background information effectively in the visible light image.
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