Research Article
Second-Order Regression-Based MR Image Upsampling
Algorithm 1
Regression-based MR image upsampling.
Input: Low-resolution image | Output: High-resolution image | Initialize: Obtain a denoised version of by using a denoising method, up-scale in the slice- | selection direction using bicubic interpolation, denote the outcome as . | Mapping function estimation: | () For each image of in the slice-selection direction, generate its smoothed version and | interpolated version , respectively. | () Partition and into image patches in raster-scan order, so as to construct the LR-HR | training set ; partition into image patches to construct the LR test patch set . | () For each , estimate the derivatives of the mapping function using Eqs. (6)–(10). | HR image estimate: | () For each patch , search its most similar patch in the LR-HR training set. | () Estimate ’s corresponding HR patch using Eq. (3). | () Generate the HR image by merging all s. | () Correct by enforcing the consistency between and . |
|