Image fusion method using non-subsampled shearlet transform and fuzzy and simple fuzzy neural network algorithms

Abstract

The methodology of combining two or more relevant images into a single highly informative image is referred to as image fusion. A new fusion methodology is introduced for combining images obtained from multiple cameras using nonsubsampled shearlet transform (NSST), fuzzy logic and a simple fuzzy neural network (SFNN). The shearlet transform combines the power of multi-scale methods with a unique ability to capture the geometry of multi-dimensional information and is efficient in representing images containing edges. The unique characteristic of shearlets is the utilization of shearing to control directional selectivity, as opposed to rotation utilized by curvelets. The shearlets are not tight edges and therefore it is necessary to perform the synthesis process by iterative methods. A new method, NSST, is introduced for multi-resolution decomposition of input images is introduced. The pixel-based fusion is performed by using fuzzy logic of NSST low-pass coefficients to generate superior quality. The region-based technique is performed by using the SFNN of NSST high-frequency directional coefficients. The SFNN exquisite the set of exemplar input feature vectors and centers a Gaussian function on each remaining one and saves its output label.

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“…It consists of multiscale decomposition using non-subsampled Laplacian pyramid transform and multidirectional decomposition using Shear filter with translation invariance. The Shear filter overcomes the limitation of the number of directions which exists in non-subsampled Contourlet [57,58].…”