Super resolution using gaussian regression
View Patent ↗A computer implemented technique for producing super resolution images from ordinary images or videos containing a number of images wherein a number of non-smooth low resolution patches comprising an image are found using edge detection methodologies. The low resolution patches are then transformed using selected basis of a Radial Basis Function (RBF) and Gaussian process regression is used to generate high resolution patches using a trained model. The high resolution patches are then combined into a high resolution image or video.
1. A computer-implemented method for generating a super-resolution image from a low resolution image, said method comprising the steps of:
determining a set of non-smooth low resolution patches of the low resolution image;
generating—using Gaussian process regression—a set of high-resolution patches for the low resolution image;
combining the high-resolution patches; and
generating a high-resolution image from the combined patches.
2. The computer-implemented method of claim 1 further comprising the step of
transforming the low-resolution patches using selected bases of radial basis function kernels.
3. The computer-implemented method of claim 2 further comprising the step of:
training a model on the low-resolution image thereby generating a trained model.
4. The computer-implemented method of claim 3 wherein said training step comprises the steps of:
generating a lower resolution image from the low-resolution image.
generating a set of high/low resolution patch pairs;
selecting a set of RBF bases; and
training a Gaussian process regression model thereby generating the trained model.
5. The computer-implemented method of claim 4 whereby the transforming step and the Gaussian process regression step(s) are performed simultaneously for a number of patches.