Improved Image Pansharpening Technique using Nonsubsampled Contourlet Transform with Sparse Representation

Multispectral (MS) and panchromatic (PAN) images contains complementary information. High spatial and spectral resolution is a prerequisite for images to be useful, which can be achieved through image pansharpening. In this paper, we propose a new pansharpening technique which is a combination of nonsubsampled contourlet transform (NSCT) and sparse representation (SR), called NSCT–SR. NSCT is a shift-invariant version of the contourlet transform which combines nonsubsampled pyramid (NSP) and the directional filter banks. NSP splits input MS and PAN images into low-pass and high-pass sub-bands. Fusion of high-pass sub-bands is done using local energy information while low-pass sub-bands are fused using SR. Finally, fused low-pass and high-pass sub-bands are combined to obtain image with high spatial and high spectral resolution. We have quantitatively compared NSCT–SR with other multiresolution algorithms by calculating spatial and spectral quality parameters. It is observed that spatial quality is improved by 0.93 % (for seaside image) and 1.54 % (for urban image). While spectral quality is improved maximum up to 31.39 and 40.47 %, for respective images. NSCT–SR also compared with other state-of-art algorithms by calculating various performance parameters including quality with no reference. It is found that, overall; NSCT–SR performs better compared to algorithms considered in work.