A Novel Image Fusion Algorithm based on PCNN and Contrast

The proposed new fusion algorithm is based on the improved pulse coupled neural network (PCNN) model and the contrast of the image. Compared with the traditional algorithms where the linking strength of each neuron is the same and its value is chosen through experimentation, this algorithm uses the contrast of each pixel as its value, so that the linking strength of each pixel can be chosen adaptively. After the processing of PCNN with the adaptive linking strength, new fire mapping images are obtained for each image taking part in the fusion. The clear objects of each original image are decided by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a new clear image. The compare-selection operator uses not only the fire time, but also the improved consistency check of the neighborhood of the certain pixel. Furthermore, by this algorithm, other parameters, for example, Delta, the threshold adjusting constant, only have a slight effect on the new fused image. Therefore, it overcomes the difficulty in adjusting parameters in PCNN. Experiments show that the proposed algorithm works better in preserving the edge and texture information than the wavelet transform method and the Laplacian pyramid method do in image fusion