Fusion method for infrared and visible images by using non-negative sparse representation

Abstract In this paper, an interesting fusion method, named as NNSP, is developed for infrared and visible image fusion, where non-negative sparse representation is used to extract the features of source images. The characteristics of non-negative sparse representation coefficients are described according to their activity levels and sparseness levels. Multiple methods are developed to detect the salient features of the source images, which include the target and contour features in the infrared images and the texture features in the visible images. The regional consistency rule is proposed to obtain the fusion guide vector for determining the fused image automatically, where the features of the source images are seamlessly integrated into the fused image. Compared with the classical and state-of-the-art methods, our experimental results have indicated that our NNSP method has better fusion performance in both noiseless and noisy situations.

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