Nonconvex Nonlocal Tucker Decomposition for 3D Medical Image Super-Resolution - Jia, Huidi; Chen, Xi'ai; Han, Zhi; Liu, Baichen; Wen, Tianhui; Tang, Yandong (Frontiers in Neuroinformatics, 2022)
Limited by hardware conditions, imaging devices, transmission efficiency and other factors, high-resolution images cannot be obtained directly in clinical settings. It is expected to obtain high-resolution images from low-resolution images for more detailed information. In this paper, we propose a novel super-resolution model for single 3D medical images. In our model, nonlocal low-rank tensor Tucker decomposition is applied to exploit the nonlocal self-similarity prior knowledge of data. Different from the existing methods that use a convex optimization for tensor Tucker decomposition, we use a tensor folded-concave penalty to approximate a nonlocal low-rank tensor. Weighted 3D total variation is used to maintain the local smoothness across different dimensions. Extensive experiments show that our method outperforms some state-of-the-art methods on different kinds of medical images, including MRI data of the brain and prostate and CT data of the abdominal and dental.