New digital images magnification algorithm based on integration of mapping and synthesis concept
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Date
2016-09-01
Authors
Rawia M.A. Abu Amro
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Abstract
Image magnification is the process of reconstructing High Resolution (HR) image from its Low Resolution (LR) version. Image magnification process is one of the most important processes that is used to fulfill human needs. This process is used in several applications such as in medical imaging, remote sensing, enhancing image details and printing. In general, the common magnification algorithms employ interpolation concept. However, these interpolation-based magnification algorithms suffer from the appearance of undesirable artifacts in magnified images such as edge blocking and edge blurring. These artifacts mostly appear around the strong edges.Therefore, instead of employing interpolation concept, this study focuses in introducing new magnification algorithm based on synthesis concept. As the synthesis concept has been used in patch based texture synthesis algorithms, a modification to the patch based texture synthesis algorithms has to be carried out in order to use it for the image magnification purpose. The proposed modification produces a new magnification algorithm called the Mapping Based Magnification Algorithm (MBMA). The proposed MBMA replaces each pixel in the LR image with a two dimensional HR block to reconstruct the HR image. The proposed algorithm is basically designed to preserve the strong edges. Two variants of the proposed MBMA are introduced, namely MBMA_Average and MBMA_Direct.The proposed MBMA variants have been compared with other state-of-the-art magnification algorithms by using 100 standard images and 200 Malaysian License Car Plate (MLCP) images. The proposed MBMA_Average produces the best magnified images with less undesirable artifacts (i.e. less of edge blurring and edge blocking) compared with the other state-of-the-art algorithms. Furthermore, the quantitative analyses show that the proposed MBMA_Average also produces the best value of the PSNR, MSE, SSIM and FSIM measurements compared to those algorithms.