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Abstract
High resolution (HR) images in biomedical applications are frequently required because improvement of pictorial information will facilitate the doctor’s diagnosis. The increase of the spatial resolution implies upgrading the imaging system but this is limited by the cost and subsequent changes in the existing parameters. In this work we use another way to address this problem is to accept the Low Resolution (LR) images from low quality imaging systems and apply the idea of post-processing to increase the resolution of the scans after acquisition. These techniques are referred to Super-Resolution (SR) reconstruction. Our goal is combining multiple low resolution images to construct a single high resolution image .The low resolution images are shifted relative to each other, blurred, down-sampled and noisy versions of the original high resolution scene. Non-Uniform Interpolation algorithm which is one of SR algorithms is used to produce HR image from LR images of the same scene