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.

我们提出了一种学习方法，能够在保留图像特性的情况下，对带有噪声的低分辨率图像进行超分辨率处理。通过定量度量（如归一化交叉相关性、归一化均方误差、峰值信噪比、结构相似度特征相似度、通用图像质量等），我们训练网络提升低分辨率噪声图像的分辨率，同时保留其特性。实验结果表明，与现有的学习方法相比，我们的方法在不同噪声类型和上采样因子下，改善了噪声图像的高分辨率预测，并且超过了学习方法并与标准方法相媲美，保留了图像的轮廓、亮度和纹理，并减少了图像的伪影。