  Several applications require the super-resolution of noisy images and the
preservation of geometrical and texture features. State-of-the-art
super-resolution methods do not account for noise and generally enhance the
output image's artefacts (e.g., aliasing, blurring). We propose a
learning-based method that accounts for the presence of noise and preserves the
properties 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-resolution
noisy image while preserving its properties. We perform our tests on the Cineca
Marconi100 cluster, at the 26th position in the top500 list. The experimental
results show that our method outperforms learning-based methods, has comparable
results with standard methods, preserves the properties of the input image as
contours, brightness, and textures, and reduces the artefacts. As average
quantitative metrics, our method has a PSNR value of 23.81 on the
super-resolution of Gaussian noise images with a 2X up-sampling factor. In
contrast, previous work has a PSNR value of 23.09 (standard method) and 21.78
(learning-based method). Our learning-based and quality-preserving
super-resolution improves the high-resolution prediction of noisy images with
respect to state-of-the-art methods with different noise types and up-sampling
factors.
