Multi-scale image fusion scheme based on non-sub sampled contourlet transform and four neighborhood Shannon entropy scheme

Optical lenses have limited depth-of-focus, which makes it impossible to capture all significant objects in focus within single picture. Multi-focus image fusion techniques can be adopted to solve the above issue because this technique precisely selects every focused point from all parent images to create final fused image. So, the final fused image contains significantly more information regarding every salient objects. In this paper, we have proposed a novel image fusion technique for fusion of multi-focused images using non-sub sampled contourlet transform. Here, we have adopted contourlet transform due to its high edge discrimination property which enables us to capture all salient object edges from different parent images. In this approach first we have generated noise free gray scale parent images using non-linear anisotropic diffusion technique. Further, in all noise free images we have employed contourlet transform to discover all salient object edges. Later, we have used 4 neighborhood entropy calculation technique based winner-take-all approach to generate final fused image. We have also used different multi-focused image sets for experimental analysis. The outcomes of all the image fusion experiments show better performance as compared to the current-sate-of-arts.