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Super-resolution for Medical Image via Sparse Representation and Adaptive M-estimator

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Objective: The goal of super-resolution (SR) is to generate high-resolution (HR) images from low-resolution (LR) input images.

Methods: In this paper, a combined method based on sparse signal representation and adaptive M-estimator is proposed for single-image super-resolution. With the sparse signal representation, the correlation between the sparse-representation of high-resolution patches and that of low-resolution patches for the identical image is learned as a set of joint dictionaries and a set of high-resolution patches is obtained for high-and low-resolution patches. And then the dictionaries and high-resolution patches are used to produce the high-resolution image for a low-resolution single-image.

Results: At the post-processing phase, the adaptive M-estimator combining the advantages of traditional L-1 and L-2 norms is used to give further processing for the resultant high-resolution image, to reduce the artefacts by learning and reconstitution and improve the performance.

Conclusions: Three experimental results show the performance improvement of the proposed algorithm over other methods.

12 Aug, 2014
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e-Published: 11 May, 2015
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