Image fusion using dual tree discrete wavelet transform and weights optimization

Image fusion is a useful context in image processing. It goals to produce more informative image using multi-image data with different sensors. In this study, an effective approach in discrete wavelet transform domain for infrared and visible image fusion is proposed. In fact, important parts of thermal images along with details of visual image must be considered in fused images. Therefore, dual tree discrete wavelet transform is used to extract both subjects based on an optimization process. The optimization considers parts of input images with maximum entropy and minimum mean square error in fused image in comparison with both input images. Experimental results on a standard database demonstrate that proposed method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.