Two-Step Image Hallucination and Its Application to 3D Medical Image Super-resolution

Abstract

In medical diagnosis, high resolution (HR) images are indispensable for giving more correct decision. However, in order to obtain high resolution medical images, it is necessary to impose long-time, hence it leads to heavy burden to the patient. Therefore Super Resolution technique, which can generate high resolution images from low resolution images using machine learning techniques, attracts hot attention recently. Therein, face hallucination is one of widely used super-resolution methods in image restoration field. However, the conventional face hallucination generally cannot recover high frequency information. Therefore, this paper integrates a further learning step into the conventional method, and proposes a 2-step image hallucination, which is prospected to recover most high frequency information lost in the available low-resolution input. Furthermore, we apply the proposed strategy to generate the high-resolution Z-direction data using self-similarity among different direction for 3D medical MR images. Experimental results show that the proposed strategy can reconstruct promising HR coronal or sagittal plane by using available LR and HR data pairs in axial plane. Keywords-image restoration; super-resolution; medical volumetric image