Efficient multi-scale non-sub-sampled shearlet fusion system based on modified central force optimization and contrast enhancement

Abstract

Infrared, visible, and medical image fusion systems are mandatory solutions for obtaining more spatial and more spectral information in a single image for efficient object detection and medical diagnosis applications. In this paper, an optimized fusion system for multi-modality images based on the Non-sub-Sampled Shearlet Transform (NSST) with Modified Central Force Optimization (MCFO), histogram matching, and local contrast enhancement is presented. The proposed multi-modality image fusion system consists of four stages. The first stage is the image registration and then histogram matching of one of the images to the other to allow the same dynamic range for both images to minimize the fusion artifacts. The NSST is used after that to decompose the images to be fused into their coefficients. The NSST provides a better sparse image representation of highly localized coefficients, anisotropic directionality, and reduced pseudo-Gibbs artifacts. After that, the MCFO technique is used to determine the optimum decomposition level and the optimum gain parameters for the best fusion of coefficients based on certain constraints. Finally, an additional contrast enhancement process is applied on the fused image to enhance its visual quality and reinforce details. The proposed fusion system is subjectively and objectively evaluated with different fusion quality metrics including average gradient, local contrast, standard deviation, edge intensity, entropy, Peak Signal-to-Noise Ratio (PSNR), and Qab/f. Real infrared, visible, and medical datasets of different modalities are used to test the proposed system. Experimental results demonstrate that the proposed system achieves a superior performance with higher image quality, higher evaluation metrics values, and much more details in images. These characteristics help for more accuracy in applications such as object detection and medical diagnosis.