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

[1]  Mark E. Oxley,et al.  Physiologically motivated image fusion for object detection using a pulse coupled neural network , 1999, IEEE Trans. Neural Networks.

[2]  Reinhard Eckhorn,et al.  Neural mechanisms of scene segmentation: recordings from the visual cortex suggest basic circuits for linking field models , 1999, IEEE Trans. Neural Networks.

[3]  Eugene M. Izhikevich,et al.  Class 1 neural excitability, conventional synapses, weakly connected networks, and mathematical foundations of pulse-coupled models , 1999, IEEE Trans. Neural Networks.

[4]  Dai Ruo-lan,et al.  Automated image segmentation using pulse coupled neural networks and images entropy , 2002 .

[5]  Gu Xiaodong.  Image Pre-processing Based Rough Set and PCNN , 2003 .

[6]  Chuanbo Wen,et al.  Multiresolution Image Fusion Algorithm Based on , 2006 .

[7]  Shi Mei-hong.  A METHOD OF IMAGE GAUSS NOISE FILTERING BASED ON PCNN , 2002 .

[8]  Fang Qing,et al.  Contrast-Based Multiresolution Image Fusion , 2000 .

[9]  Erik Blasch,et al.  Biological information fusion using a PCNN and belief filtering , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[10]  Eugene M. Izhikevich,et al.  Weakly pulse-coupled oscillators, FM interactions, synchronization, and oscillatory associative memory , 1999, IEEE Trans. Neural Networks.

[11]  Alexander Toet,et al.  Merging thermal and visual images by a contrast pyramid , 1989 .

[12]  M. Arndt,et al.  A neural network for feature linking via synchronous activity: Results from cat visual cortex and from simulations , 1989 .

[13]  Shi Mei-hong.  Image Binary Segmentation Based on Improved PCNN Mode , 2002 .

[14]  Zhe Chen,et al.  A multisensor image fusion algorithm based on PCNN , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[15]  Zhang Jun-ying,et al.  Image Fusion Based On Pulse-Coupled Neural Networks , 2004 .

[16]  John L. Johnson,et al.  PCNN models and applications , 1999, IEEE Trans. Neural Networks.