Image fusion algorithm based on wavelet transform and fuzzy reasoning

Image fusion based on wavelet transform is the most commonly used image fusion method, which decomposes the source images, fuses their coefficients according to some fusion rules and then reconstructs the fused image. Its two main traditional rules are selecting maximum absolute value and the combination of selecting and weighted averaging. Both of the two rules did some artificial supposes to eliminate the uncertainty of the extent of each source image's contributions, so they both ignored some useful information and were sensitive to noise. Fuzzy reasoning is the best way to resolve uncertain problems. As a result, this paper proposed a new image fusion algorithm based on wavelet transform and fuzzy reasoning. It first decomposed source images through wavelet transform, computed the extent of each source image's contribution through fuzzy reasoning using the area feature of source images' wavelet coefficients, and then fused the coefficients through weighted averaging with the extents of each source images' contributions as the weight coefficients. Finally it did inverse wavelet transform to produce the fused image. Using the mutual information and PSNR as criterions, experiment results demonstrated that the new algorithm was more effective and robust than the traditional fusion algorithms based on wavelet transform.