HIGHLIGHTS
SUMMARY
Most existing deep learning methods are usually realized in a fully supervised way, and then, the learned SR model may only be applicable to the LR observations captured in controlled imaging conditions such as with the assumed degradation 2 of 13 model: "bicubic" downsampling. To handle the limitation of deeply relying on the prior training with external data, internal learning via extracting training samples from the observed LR image and its downsampled version has been exploited to produce a specific SR model for the understudied image. To solve the challenge of the blind SR . . .
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