A New Image Fusion Method based on Integration of Wavelet and Fast Discrete Curvelet Transform

Abstract

Image fusion is one of the most useful term related to digital image processing, computer vision and medical imaging. The objective of image fusion is to extract the useful information from several images into a single image. Recently, more research has been done on wavelet based image fusion methods for medical application. Wavelet transform is useful for objects with point singularities and analyses the feature of images in detailed, but it does not provide information about edges clearly. While curvelet transform is more useful for the analysis of images having curved shape edges. So, in this paper, a new image fusion method is proposed based on the integration of wavelet and fast discrete curvelet transform, which describe the curved shapes of images and analyses feature of images better. This paper uses MRI and CT images for fusion which contains complementary information helpful for diagnosis of disease. The fusion results obtained from proposed method are analyzed and compared visually and statistically with different types of wavelets used in image fusion. The results of proposed method are efficient and improve the Entropy, PSNR, Mean, STD and MSE. The proposed method can be helpful for better medical diagnosis.

Enhancement of IR Images using Homomorphic Filtering in Fast Discrete Curvelet Transform (FDCT)

“…The first generation used a complex steps involving the ridgelet analysis of radon transform of an image. The performance was slow [7] [8]. This generation based on unequally-spaced FFT.…”