Machine Learning Methods for Demosaicing and Denoising
نویسندگان
چکیده
We have implemented super-resolution techniques such as machine learning methods and solving a least squares problem with a prior to perform demosaicing. These techniques include k-nearest neighbors (KNN), linear regression, and alternating direction method of multipliers with a total variation prior (ADMM TV). For all methods, parameters were optimized to minimize the mean-squared-error (MSE) and maximize the peak signal-to-noise ratio (PSNR). The MSE and PSNR were compared to the results of traditional demosaicing methods, bilinear and Malvar interpolation.
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