Viola, F and Fitzgibbon, A and Cipolla, R (2012) A unifying resolution-independent formulation for early vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 494-501. ISSN 1063-6919Full text not available from this repository.
We present a model for early vision tasks such as denoising, super-resolution, deblurring, and demosaicing. The model provides a resolution-independent representation of discrete images which admits a truly rotationally invariant prior. The model generalizes several existing approaches: variational methods, finite element methods, and discrete random fields. The primary contribution is a novel energy functional which has not previously been written down, which combines the discrete measurements from pixels with a continuous-domain world viewed through continous-domain point-spread functions. The value of the functional is that simple priors (such as total variation and generalizations) on the continous-domain world become realistic priors on the sampled images. We show that despite its apparent complexity, optimization of this model depends on just a few computational primitives, which although tedious to derive, can now be reused in many domains. We define a set of optimization algorithms which greatly overcome the apparent complexity of this model, and make possible its practical application. New experimental results include infinite-resolution upsampling, and a method for obtaining subpixel superpixels. © 2012 IEEE.
|Divisions:||Div F > Machine Intelligence|
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|Date Deposited:||15 Dec 2015 13:37|
|Last Modified:||13 Feb 2016 01:26|