Optimizing and Learning for Super-resolution. Lyndsey C. Pickup, Stephen J. Roberts & Andrew Zisserman Robotics Research Group, University of Oxford. The Super-resolution Problem. Given a number of low-resolution images differing in: geometric transformations
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Lyndsey C. Pickup, Stephen J. Roberts
& Andrew Zisserman
Robotics Research Group, University of Oxford
Given a number of low-resolution images
Estimate a high-resolution image:
We don’t have:
parameters in the prior term at the
i.e. given the low-res images, y, we solve for the SR image xand the mappings, W simultaneously.
Huber function is quadratic in the middle, and linear in the tails.
Red: large α
Blue: small α
Probability distribution is like a heavy-tailed Gaussian.
This is applied to image gradients in the SR image estimate.
Advantages: simple, edge-preserving, leads to convex form for MAP equations.
Solutions as α and v vary:
Edges are sharper
Too much smoothing
Too little smoothing
Use first set to obtain an SR image.
Find error on validation set.
MAP version: fixing registrations then super-resolving
Joint MAP version with adaptation of prior’s parameter values