Multi-sensor image enhanced fusion algorithm based on NSST and top-hat transformation

Abstract

In this paper, a novel method based on nonsubsampled shearlet transform (NSST) and multi-scale top-hat transform (MTHT) is proposed to fuse the SAR image, infrared image and visible light image, which will produce a fused image with higher contrast and better edges. First, three original images are decomposed into low frequency subband coefficients and the bandpass direction subband coefficients by NSST. Second, the effective bright and dark image features from low-frequency coefficients of three original images are extracted by MTHT, then by using the pixel-wise maximum operation, the salient fused bright and dark features are obtained and joined into the combined low frequency coefficients. To reduce the influence of noises of SAR and infrared image on the fused image, the significant factor of local directional information entropy, which can distinguish meaningful image characteristics and noises, is employed as the measurement to select the bandpass direction subband coefficients. Finally, the fused image is obtained by the inverse NSST on the combined coefficients. The experiment results demonstrate the superiority of our method compared with conventional methods in terms of visual inspection and objective measures.