An optimized non-subsampled shearlet transform-based image fusion using Hessian features and unsharp masking

Existing image fusion approaches are not so efficient to seize significant edges, texture and fine features of the source images due to ineffective and non-adaptive fusion structure. Also for objective evaluation of fusion algorithms, there is a need of a metric to measure source image features which are preserved in the fused image. To address these issues, an optimized non-subsampled shearlet transform (NSST) is developed, which is applied to decompose the source images into low- and high frequency bands. The low frequency bands are fused using proposed descriptor obtained from superposition of scale multiplied Canny edge detector features and Hessian features. The high frequency bands are fused using unsharp masking based fusion rule. Moreover, a metric QE is formulated on the basis of Karhunen-Loeve transform (KLT). The information of image pixel variance for both source and fused images can be measured by using the proposed metric QE, and it gives an indication of the amount of variance information transferred from the source images to the fused image. Both subjective and objective analysis show the efficacy of the proposed fusion structure and the metric QE.