Comparative Evaluation of DWT and DT-CWT for Image Fusion and De-noising

In various real life applications such as remote sensing and medical image diagnosis, image fusion plays imperative role and it is more popular for image processing applications. Because of inadequate nature of practical imaging systems the capture images or acquired images are corrupted from various noise hence fusion of image is an integrated approach where reduction of noise and retaining the original features of image is essential. Image fusion is the process of extracting meaningful visual information from two or more images and combining them to form one fused image. Discrete Wavelet Transform (DWT) has a wide range of application in fusion of noise images. Previously, real valued wavelet transforms have been used for image fusion. Although this technique has provided improvements over more inhabitant methods, this transform suffers from the shift variance and lack of directionality associated with its wavelet bases. These problems have been overcome by the use of a reversible and discrete complex wavelet transform (the Dual Tree Complex Wavelet Transform DT-CWT). This paper therefore introduces an alternative structure such as DT-CWT that is more flexible, highly directional and shift invariant which outperforms the conventional method in terms of PSNR and image quality improvement. General Terms Image, Wavelet, Fusion of image. Image fusion rule, Denoising.