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Several applications require the super-resolution of noisy images and thepreservation of geometrical and texture features. State-of-the-artsuper-resolution methods do not account for noise and generally enhance theoutput image's artefacts (e.g., aliasing, blurring). We propose alearning-based method that accounts for the presence of noise and preserves theproperties of the input image, as measured by quantitative metrics (e.g.,normalised crossed correlation, normalised mean squared error,peak-signal-to-noise-ration, structural similarity feature-based similarity,universal image quality). We train our network to up-sample a low-resolutionnoisy image while preserving its properties. We perform our tests on the CinecaMarconi100 cluster, at the 26th position in the top500 list. The experimentalresults show that our method outperforms learning-based methods, has comparableresults with standard methods, preserves the properties of the input image ascontours, brightness, and textures, and reduces the artefacts. As averagequantitative metrics, our method has a PSNR value of 23.81 on thesuper-resolution of Gaussian noise images with a 2X up-sampling factor. Incontrast, previous work has a PSNR value of 23.09 (standard method) and 21.78(learning-based method). Our learning-based and quality-preservingsuper-resolution improves the high-resolution prediction of noisy images withrespect to state-of-the-art methods with different noise types and up-samplingfactors.

Learning-Based and Quality Preserving Super-Resolution of Noisy Images