Super-Resolution from a Single Medical 3D Image Data Set

Super-Resolution Reconstruction is a technique for recovering and reconstructing a higher resolution of a low-resolution image. There exist classical and examplebased super-resolution reconstruction methods, and especially a combination of them leads to reasonable results. In this thesis, we combine a classical and an example-based super-resolution reconstruction method for enhancing the resolution of medical image data sets. Moreover, we present a super-resolution reconstruction method for solving the anisotropy problem in CT and MRI 3D image data sets. Our patch-based method improves the quality of low-resolution structures in anisotropic slices using high-quality structures present in isotropic slices of the data set. Consequently, an isotropic super-resolution image is calculated for each anisotropic slice of the data set. For our approach, no further images with subpixel misalignments are necessary as usually needed by classical superresolution strategies, but the information available in the 3D data set suffice for calculating reasonable super-resolution images. Various experiments with real medical data sets prove the eficiency and correctness of our implementation. We are successful in approximating the super-resolution of medical images reasonably, and improving their quality significantly