Innovative image fusion algorithm based on fast discrete curvelet transform with different fusion rules

The image fusion algorithm based on wavelet transform works successfully for linear objects but its basic limitation arises for fusion of curved shapes. It is observed that most of medical images contain curved shape objects. In this paper Innovative Image fusion algorithm based on Fast Discrete Curvelet transform with different Fusion Rules such as Minimum Selection, PCA based rule, Averaging rule, Maximum selection rule and Laplacian pyramid rule is implemented and experimental results from different fusion rules are compared with each other. The performance evaluation is done by considering 7 quality metrics parameters like Mean, Standard deviation, Entropy, Average Gradient, Correlation coefficient, RMSE and PSNR etc which proves improved performance than Wavelet transform and other Curvelet transforms in terms of visual quality and information content of fused image. The results obtained can be helpful for medical diagnosis of patient for further treatment.