Results For Image Fusion. This single image is more informative and accurate than any single source image and it consists of all the necessary information. IMAGE FUSION PERFORMANCE ANALYSIS BY ANOVA NATALIA R.
Image fusion was developed and evaluated and the results were compared with pixel level image fusion algorithms using fusion quality evaluation metrics. The terms commonly used in the remote sensing community vary. In addition to medical reasons there exists technical challenges in image registration and fusion resulting from image noise resolution difference between images inter-image variability between the images lack of sufficient number of images per modality high cost of imaging and increased computational complexity with increasing image space and time resolution.
It was concluded that feature level image fusion provides better fusion results at the cost of execution time.
Figure 2a and 2b shows The curvelet transform is an advanced tool for graphical original input MRI and CT images used as input for various application for representation of curved shapes which can be algorithm for image fusion figure 2c 2d 2e and 2f shows results of image fusion obtained using PCA DWT SWT and extended to the fields of. A bigger value shows good fusion results. The purpose of image fusion is not only to reduce the amount of data but also to construct images that are more appropriate and understandable for the human and machine perception. RITHMS FOR SWT BASED IMAGE FUSION Source Images Fusion Rule RMSE PSNR in dB Tower bridge Energy 942 2865 Tower bridge Gaussian filtering 716 3103 Cameraman Energy 878 2925 Cameraman Gaussian filtering 675 3154 Peppers Energy 782 3030 Peppers Gaussian filtering 582 3290 Lenna Energy 618 3230 Lenna Gaussian filtering 487 3440.