Super resolution OF 3D MRI images using a Gaussian scale mixture model constraint

In multi-slice magnetic resonance imaging (MRI) the resolution in the slice direction is usually reduced to allow faster acquisition times and to reduce the amount of noise in each 2-D slice. In this paper, a novel image super resolution (SR) algorithm is presented that is used to improve the resolution of the 3D MRI volumes in the slice direction. The proposed SR algorithm uses a complex wavelet-based de-blurring approach with a Gaussian scale mixture model sparseness constraint. The algorithm takes several multi-slice volumes of the same anatomical region captured at different angles and combines these low-resolution images together to form a single 3D volume with much higher resolution in the slice direction. Our results show that the 3D volumes reconstructed using this approach have higher quality than volumes produced by the best previously proposed approaches.