High Image Resolution Using Curvelet and Contourlet Transform for Bio-medical Applications

The goal of image fusion is to integrate complementary information from multi focus information such the new pictures area unit additional appropriate for the aim of computer-processing tasks like segmentation and feature extraction. In This work presents a picture fusion theme that is predicated on the Curvelet model (DCT). The curvelet transforms of the input pictures area unit fittingly combined, and therefore the new image is obtained by taking the inverse Curvelet remodel of the united rippling coefficients. Associate in nursing area unite-based most choice rule and a consistency verification step are used for feature choice. The projected theme performs higher than the Transform strategies as a result of the compactness, directional property, and orthogonality of the curvelet remodel. A performance live mistreatment specially generated check pictures is usually recommended and is employed within the analysis of various fusion strategies, and in examination the deserves of various rippling remodel kernels. Intensive experimental results together with the fusion of multi focus pictures, Landsat and Spot pictures, Landsat and Sea sat SAR pictures, IR and visual pictures, and magnetic resonance imaging and PET pictures area unit bestowed within the paper.