Super-resolution for Medical Image via Sparse Representation and Adaptive M-estimator
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1Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430000, China.
Objective:
The goal of super-resolution is to generate high-resolution images from low-resolution 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. 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 L1 and L2 norms, is used to give further processing for the resultant high-resolution image, to reduce the artefact by learning and reconstitution, and improve the performance.
Conclusion:
Three experimental results show the performance improvement of the proposed algorithm over other methods.
