Texture hallucination for large-scale image super-resolution

A method of training an artificial neural network (ANN) for image upscaling, the method comprising:

• extract an upscaled feature map from an input image;
• match the input image to a portion of a reference image, wherein a resolution of the reference image is higher than a resolution of the input image;
• concatenate the upscaled feature map with a reference feature map corresponding to the portion of the reference image to produce a concatenated feature map;
• generate a reconstructed image based on the concatenated feature map using the ANN;
• compute a texture loss based on a high frequency band filter and a degradation loss based on a downscaled version of the reconstructed image;
• and
• update the ANN based on the texture loss and the degradation loss.

An apparatus for image upscaling, comprising:

• an upscaling component configured to extract an upscaled feature map from an input image;
• a matching component configured to match the input image to a portion of a reference image, wherein a resolution of the reference image is higher than a resolution of the input image;
• a concatenation component configured to concatenate the upscaled feature map with a reference feature map corresponding to the portion of the reference image to produce a concatenated feature map;
• and
• a reconstruction component configured to generate a reconstructed image based on the concatenated feature map, wherein the reconstruction component is trained with a texture loss and a degradation loss, wherein the texture loss is based on a high frequency band filter, and the degradation loss is based on a downscaled version of the reconstructed image.