Gradient Profile Based Super Resolution of MR Images with Induced Sparsity

摘要

Trade-off between resolution and signal to noise ratio(SNR) of magnetic resonance (MR) images can be improved by post processing algorithms to provide high quality MR images required for several medical diagnosis. This paper proposed a constraint to sharpen the gradient profile (GP), typically symbolizes the quality of image, of super-resolved MR images in the framework of sparse representation based super resolution without any external LR (low-resolution)-HR (high resolution) pair images. It has been performed by establishing a piecewise linear relation between GP of LR image up-scaled by Bi-cubic interpolation (UR), and corresponding LR image. The resultant relationship is used to approximate the ground truth HR image such that GP of upsampled LR image is improved. Further, to preserve the details along with its consistency among coronal, sagittal and axial planes, we have learned multiple dictionaries by extracting patches from the same and adjacent slices. The experimental results demonstrate that the proposed approach outperforms qualitatively and quantitatively the existing algorithms of increasing the resolution of MR images.