A novel statistical fusion rule for image fusion and its comparison in non subsampled contourlet transform domain and wavelet domain
This work addresses image fusion for applications like medical imaging and remote sensing, but it appears incremental as it builds on existing transform domains with a new statistical rule.
The authors tackled the problem of image fusion by proposing a Weighted Average Merging Method (WAMM) in the NonSubsampled Contourlet Transform (NSCT) domain, and found that it outperformed wavelet domain methods by better preserving edges and visual quality in fused images.
Image fusion produces a single fused image from a set of input images. A new method for image fusion is proposed based on Weighted Average Merging Method (WAMM) in the NonSubsampled Contourlet Transform (NSCT) domain. A performance analysis on various statistical fusion rules are also analysed both in NSCT and Wavelet domain. Analysis has been made on medical images, remote sensing images and multi focus images. Experimental results shows that the proposed method, WAMM obtained better results in NSCT domain than the wavelet domain as it preserves more edges and keeps the visual quality intact in the fused image.