A Novel Approach for Super Resolution in Medical Imaging

The recent increase in the wide use of digital imaging technologies in consumer (e.g., digital video) and other areas (e.g., security and military) has brought with it a simultaneous demand for higher-resolution images. The demand for such high-resolution (HR) images can be met by algorithmic advances in super-resolution (SR) technology intended with hardware development. Such HR images not only give the viewer a more pleasing picture but also offer additional details that are important for subsequent analysis in many applications. Therefore, a resolution enhancement (super-resolution) approach using computational, mathematical, and statistical techniques has received a great deal of attention recently. One promising approach is to use signal-processing techniques to obtain an HR image (or sequence) from observed multiple low- resolution (LR) images. The major advantage of this approach is that it may cost less and the existing LR imaging systems can still be utilized. These High resolution images are frequently required in biomedical applications, because the HR images provide the accurate spatial and intensity information for correct diagnosis. In various MR imaging techniques, which are important for early medical diagnosis purposes.