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Image fusion is aimed at combining source images of the same scene into a single fused image with more reliable and precise information, which is suitable for further image-processing tasks. Here we present a novel method for fusing infrared and visible images, which is based on nonsubsampled shearlet transform (NSST) and block compressive-sensing sampling (BCSS). Compressive-sensing approach is widely used in various fields and constitutes a basis of low-signal sampling compression method employed under conditions of sparsity of signals. We investigate the BCSS technique in image fusion and suggest a fusion method where the NSST is applied for decomposition and reconstruction, and two-measure forms of local region energy are presented for maximum-choosing fusion rules for the NSST subband coefficients. Our approach is tested on five standard datasets of infrared and visible images. The results are compared with those obtained using several common fusion methods. The experimental results illustrate validity and efficiency of our method. Both the efficiency and robustness of the fusion are analyzed using a number of image-quality measures.

Fusion of infrared and visible images based on nonsubsampled shearlet transform and block compressive sensing sampling