Image fusion based on visual salient features and the cross-contrast

• Low frequency subband coefficients are selected based on visual salient features.
• Bandpass directional subband coefficients are selected by the cross-contrast.
• Three maps of visual salient features are constructed based on visual saliency.

To extract and combine the features of the original images, a novel algorithm based on visual salient features and the cross-contrast is proposed in this paper. Original images were decomposed into low frequency subband coefficients and bandpass direction subband coefficients by using the nonsubsampled contourlet transform. Three maps of visual salient features are constructed based on visual salient features the local energy, the contrast and the gradient respectively, and low-frequency subband coefficients are got by utilizing these visual saliency maps. The cross-contrast is obtained by computing the ratio between the local gray mean of bandpass direction subband coefficients and the local gray mean of fused low-frequency subband coefficients. Bandpass direction subband coefficients is goted by the cross-contrast. Comparison experiments have been performed on different image sets, and experimental results demonstrate that the proposed method performs better in both subjective and objective qualities.