Абстрактный

Development of Image Fusion Algorithms by Integrating PCA, Wavelet and Curvelet Transforms

M.Masthanaiah, P. Janardhan Sai Kumar

Image fusion is the process of combining the relevant information from two or more images into a single highly informative image. The resulting fused image contains more information than the input images. In this paper, different methods namely Averaging method, Principal Component Analysis, Different Wavelet Transforms and Curvelet Transform were used to fuse different modality of images [e.g., MRI, CT; MULTI-SPECTRAL, PANCHROMATIC etc.] and all the fused images were compared using different comparison techniques namely Mean, Standard Deviation, Entropy (H) , Correlation Coefficient(CC), Co-Variance, Root Mean Square Error(RMSE), Peak Signal To Noise Ratio(PSNR). In addition to this, different wavelet transforms were integrated with PCA to improve performance evaluation. The wavelet Transform methods used here are Haar wavelet, daubechis wavelet, Bi-Orthogonal wavelet, discrete Meyer wavelet methods etc.