Aiming at the problem that single transillumination energy cannot completely cover all the information for the digital radiography （DR） of complex structures with large thickness ratios， we propose a pulse coupled neural network （PCNN） image fusion algorithm based on regional characteristics and take aero-engine turbine blades as the research objects. First， the multiple incremental tube voltage transillumination sub-images are decomposed into low frequency sub-bands and high frequency sub-bands at multiple scales by the non-sub-sampled contourlet transform （NSCT）. Second， the PCNN algorithm is deployed to adjust the connection strength of the directions that hold the most obvious characteristics in the improved spatial frequency of each sub-band. Third， to fulfill the external excitation， the low-frequency sub-bands are calculated by the regional mean square error， while the high-frequency sub-band by the sum-modified Laplacian. Thus the two results are processed through the fire mapping by following the maximum principle. Finally， the fusion images are obtained by the NSCT inverse transformation. The experimental results show that the proposed method can improve fusion results in terms of entropy， standard deviation， average gradient， clarity and spatial frequency， compared with classical fusion algorithms including the methods based on the Laplace pyramid transformation. Our method can extend image-fusion performance by enriching the detailed information of the images and obtaining higher quality.