An Image Fusion Algorithm Based On Contourlet Transform

Image fusion, which is an important and useful technique for image analysis and computer vision in recent years, is a technique to combine multiple images of the same scene into a new one. This technique has been widely used not only in military application, but also in industry and agriculture fields, such as resources management, town planning, weather forecast and geological analysis. With studying the multi-resource image fusion methods on the base of transform domain and fusion methods, a new improved PCNN fusion algorithm is proposed in this thesis. By comparing the firing times of different PCNN, we can get better coefficients, keep the texture details efficiently and improve the fusion results a lot.In the first part of this thesis, image fusion algorithm based on wavelet transform and its processing schedule is introduced, and the factors of impacting the fusion results are analyzed. Wavelet analysis has many advantages on one dimension image processing because of its multi-resolution and non-redundancy. However, this advantage can not be applied in two dimensional applications because of its characters in dealing with multi-dimensional images. On the above analysis, the Contourlet transform is put forward for its multi-resolution and directional advantages, which shows its benefits on image processing domain.Secondly, multi-scale transform methods are discussed particularly in this thesis. And we conclude that the Contourlet transform is better than the wavelet transform by the comparing experiments on ideal images. The nonsubsampled Contourlet transform and its use of image fusion is also introduced.What's more, on comparing the fusion methods, in order to overcome problems of low correlation and detail keeping, a new improved paralleled PCNN fusion algorithm is proposed in this thesis, which can efficiently keep the detail and edge information effectively.Finally, to get through the limitations of the fusion evaluation rules, a new evaluation rule is presented, with which the final results can be analyzed using both the subjective evaluation method and the result data. By the comparison of the results of PCNN fusion rules on different fire times, the new improved PCNN fusion method performs much better than the traditional methods in details. After comparing its applications on multi-focus images and multi-spectrum images, the algorithm proposed shows its superior on multi-spectrum image fusion.