Image Fusion Technique Based on PCA and Fuzzy Logic

Alignment is the superposition of images of two or more same scenes taken at different times or with different sensors. Registration is an important step in many image analysis tasks such as fusion of images, detection of change. In this regard, the ground reference point technique is used to register the MS image as a pan image as a reference image. The ground reference point method is described as a point on the earth that is used as a known position for geographical alignment of a scene image. All MS images of this article are already registered. Pan image is used as reference image.

Medical image fusion plays an important role in clinical diagnosis. This paper proposed a sparse representation theory and a multimode multidose medical image combination fusion model based on PCA. Visual and quantitative experimental results showed that this method effectively demonstrates the geometric shape and edges and prove that its performance is superior to PCA and OMP fusion. The modality can also be extended to merge multiple source images from multiple resolutions, multiple spectral frequencies, and multiple modalities.

In this research, we propose a fusion method using PCA transformation and sparse transformation. Please make effective use of the advantages of PCA and sparse fusion scheme. The proposed fusion framework is shown in FIG. First, we extract common innovative components from multiple images of geometric arrangement of the same scene. Secondly, different fusion rules are used to fuse these coefficients. To test the performance of the proposed joint fusion algorithm, we compared the quantitative and qualitative results with the two prior art methods. Qualitative measurements are made by visual inspection taking into account transparency and noise suppression. Since the proposed joint fusion uses both the PCA domain and the sparse domain, we use PCA which is the sparse OMP fusion method for comparison. For evaluation, we used a joint strategy for multiple resolution, multiple focus and multimode images, and compared the results with existing algorithms.

FIG. 4 shows the results of applying two multimodal medical images to three image fusion algorithms. Medical images are MRI and CT images of the same scene as those registered. CT images provide information on bone structure and MRI images contain organization information. Since medical image fusion is used for diagnosis, high accuracy is required. Therefore, multimodal image fusion provides sufficient detail for diagnosis. Based on visual inspection, details of Joint Sparse results are included. Although the PCA results appear to have high spatial resolution, it is disappointing for detailed information. Bone details are not displayed in the PCA result image. Although the sparse results are better, you can see that some artifacts look more accurate by reconstructing the fusion image with the joint fusion algorithm.