LAUSR

Abstract. The fusion of infrared and visible images may result in low contrast, which is unsuitable for observation by human eyes. Thus, we propose a contrast-enhanced fusion algorithm with nonsubsampled shearlet transform (NSST) frames, in which the NSST is first employed to decompose each of the source images into one low frequency sub-band and a series of high frequency sub-bands. To improve the fusion performance, we designed two measures for fusion of the low frequency and the high frequency: the low frequency is divided into salient and nonsalient regions in accordance with the human visual system to improve the global contrast by targeted fusion and the high frequency requires a local contrast fusion strategy. Finally, the merged sub-bands are constructed according to the selection principles, and the final fused image is produced by applying the inverse NSST on these merged sub-bands. Experimental results demonstrate the effectiveness and superiority of the proposed method over the state-of-the-art fusion methods in terms of both visual effect and objective evaluation results.