Edge-preserving MRI Super Resolution using a high frequency regularization technique

The resolution of MRI images is limited due to several factors such as imaging hardware or time constraints. However, high MRI image resolution is desired in many medical applications. Traditional Super Resolution (SR) algorithms are generally unable to recover the high frequency (HF) information of MRI images. Recently, spatial adaptive SR algorithms have utilized the combined edge preserving and smoothness constraint methods to improve the quality of the images. Segmenting the image into edge and smooth blocks is a common step which adds to the complexity and execution time of these methods. This paper presents a fast SR technique for MRI images that preserve the edges and improve the visual quality of MRI images without segmenting the images. Experimental results prove the ability of this proposed approach with respect to the traditional SR methods in terms of better visual quality and less execution time.