Scaling up of Low Resolution Images using Super Resolution Techniques & Performing Intensity Correction for Medical Imaging
DOI:
https://doi.org/10.14738/jbemi.26.1732Keywords:
Intensity inhomogeneity, Super Resolution, Dictionary learning, Sparse Representation, MRIAbstract
The diagnosis of illness and certain underlying conditions can be effectively done by the use of Medical imaging. Higher resolution images are used to increase the diagnostic capabilities of the medical practitioner which leads to early and effective diagnosis of an ailment. Although a lot of advanced devices like Computerized Tomography (CT), Magnetic Resonance Imaging (MRI) etc. are currently available, the problem of Noise, Blur limits the overall ability of these devices to produce higher resolution images. A solution which can be proposed is the use of Super Resolution (SR) techniques which can be used for processing of such images. In this paper we make use of intensity correction of input medical images. The super resolution methods are done patch wise. The input images are divided into patches and dictionaries containing high and low resolution patches are obtained. Using the k-SVD algorithm for dictionary learning and OMP method for image super resolution reconstruction, the final high resolution image is obtained. The combination of intensity correction and super resolution leads to computationally and visually better results.
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