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The objective o f this paper is to estimate a high resolution medical image from a single noisy low resolution image with the help of given database of high and low resolution image patch pairs. Initially a total variation (TV) method which helps in removing noise effectively while preserving edge information is adopted. Further denoising and super resolution is performed on every image patch. For each TV denoised low – resolution patch, its high – resolution version is estimated based on finding a non negative sparse linear re presentation of the TV denoised patch over the low – resolution patches from the database, where the coefficients of the representation strongly depend on the similarity between the TV denoised patch and the sample patches in the database. The problem of fin ding the non negative sparse linear representation is modeled as a non negative quadratic programming problem. The proposed method is especially useful for the case of noise – corrupted and low – resolution image.

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