Blind super-resolution using a learning-based approach

The super-resolution of a single image of unknown point spread-function (PSF) is addressed by extending a learning framework using blind deconvolution with an uncertainty around the resulting PSF. Results indicate success in refining the estimate of the PSF as well as to restoring the image. A novel disparity measure is also proposed to quantify the results.

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