In the current study, we were inspired by sparse analysis signal representation theory\nto propose a novel single-image super-resolution method termed â??sparse analysis-based super\nresolutionâ? (SASR). This study presents and demonstrates mapping between low and high resolution\nimages using a coupled sparse analysis operator learning method to reconstruct high resolution (HR)\nimages. We further show that the proposed method selects more informative high and low resolution\n(LR) learning patches based on image texture complexity to train high and low resolution operators\nmore efficiently. The coupled high and low resolution operators are used for high resolution image\nreconstruction at a low computational complexity cost. The experimental results for quantitative\ncriteria peak signal to noise ratio (PSNR), root mean square error (RMSE), structural similarity index\n(SSIM) and elapsed time, human observation as a qualitative measure, and computational complexity\nverify the improvements ordered by the proposed SASR algorithm.
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