CPC G06T 5/005 (2013.01) [G06T 5/10 (2013.01); G06T 5/20 (2013.01); G06T 2207/20048 (2013.01); G06T 2207/20081 (2013.01)]  20 Claims 
1. A computerimplemented method comprising:
obtaining an input image tensor of image data, the input image tensor comprising a known region and a missing region, wherein the known region comprises a plurality of known pixel data units and the missing region comprises a plurality of unknown pixel data units;
transforming the input image tensor of the image data into a spectral tensor of the image data wherein one or more of: row values or column values, of each matrix in the input image tensor are provided as a sequence of pixel data values in a spatial domain for which corresponding frequency values are determined in a frequency domain;
modifying the spectral tensor of the image data to generate a filtered spectral tensor of the image data at least by applying a filter, having a plurality of weights, on the spectral tensor;
wherein one or more weights of the plurality of weights of the filter are determined at least in part by a machine learning algorithm;
generating output image data, at least in part, by transforming the filtered spectral tensor into the spatial domain;
using the known region of the input image tensor and based, at least in part, on a region, of the output image data, that corresponds to the missing region of the input image tensor, generating a final output image.
