LRTV: MR Image Super-Resolution with Low-Rank and Total Variation RegularizationsReport as inadecuate




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1 BRIC - Biomedical Research Imaging Center North Carolina

Abstract : Image super-resolution SR aims to recover high-resolution images from their low-resolution counterparts for improving image analysis and visualization. Interpolation methods, widely used for this purpose, often result in images with blurred edges and blocking effects. More advanced methods such as total variation TV retain edge sharpness during image recovery. However, these methods only utilize information from local neighborhoods, neglecting useful information from remote voxels. In this paper, we propose a novel image SR method that integrates both local and global information for effective image recovery. This is achieved by, in addition to TV, low-rank regularization that enables utilization of information throughout the image. The optimization problem can be solved effectively via alternating direction method of multipliers ADMM. Experiments on MR images of both adult and pediatric subjects demonstrate that the proposed method enhances the details in the recovered high-resolution images, and outperforms methods such as the nearest-neighbor interpolation, cubic interpolation, iterative back projection IBP, non-local means NLM, and TV-based up-sampling.

Keywords : Image enhancement spatial resolution low rank matrix completion image sampling sparse learning





Author: Feng Shi - Jian Cheng - Li Wang - Pew-Thian Yap - Dinggang Shen -

Source: https://hal.archives-ouvertes.fr/



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