Multifocus Image Fusion Algorithms using Dyadic non-subsampled Contourlet Transform

The dyadic wavelet has good multi-scale edge detection and sub-band correlation features. Contourlet transformation has multi-directional characteristics. So a new dyadic nonsampling contourlet transformation is constructed. Firstly, multi-scale decomposition is performed on source images using dyadic contourlet transform to get high-frequency and low-frequency images. And then, according to the different region statistics between high-frequency and low-frequency, the fused coefficients in contourlet domain are obtained by using different fusion rules. Finally, the inverse wavelet based contourlet transform is utilized to obtain fused image. Low-frequency sub-band coefficient used the choice or weighted method according to regional similarity measure, and in accordance with the edge-dependent fusion quality index to determine the weight of edge information. For the edge of high-frequency sub-band, the fusion rule uses the largest absolute value method, and the non-edge part selects the sub-band coefficients of clear region. The experimental results show that the proposed method outperforms other conventional wavelet methods. At the same time, it can extract all useful information from the original images and improve fusion quality.