Infrared and visible image fusion via hybrid decomposition of NSCT and morphological sequential toggle operator

Infrared and visible image fusion technique is beneficial to improve scene description capability and target detection accuracy for modern image processing. In this paper, we propose a novel and effective image enhanced fusion via a hybrid decomposition of non-subsample contourlet transform (NSCT) and morphological sequential toggle operator (MSTO). MSTO is constructed as a multi-scale decomposition on the base of top-hat transformation. We employ MSTO to extract the bright/dark image features (BIF/DIF) from approximation subband of NSCT decomposition. This hybrid decomposition can effectively suppress the noise and pseudo-edge of source images. The extracted BIF and DIF are fused with maximum selection rule based on local energy map at different scales. Meanwhile, the guided filter is used to enhance the fused BIF and DIF. These enhanced fused BIF and DIF are integrated into the combined approximation subband, which can largely improve the contrast and visible effect of final fusion image. Our experiments demonstrate that this proposed approach is superior to other fusion methods in terms of visual inspection and objective measures.