An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

Abstract

Image fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.Keywords: region based energy method • discrete wavelet transform • discrete fast curvelet transform • root mean square error © Versita sp. z o.o.

Implementation analysis of pixel‐level image processing based on multiscale transforms

“…Sumathi et al 29 using WT decompose the primary images to a proper level, acquiring one low-frequency approximate component and three high-frequency detailed components in every level. CT will then be used on approximate components at low frequency and detailed components at high frequency from the images got from the previous step.…”