Image super-resolution algorithms generally use the patch-based processing which is very computationally intensive. Any alternative?

02 February 2018 1 8K Report

In general, the dictionary learning based image super-resolution methods adopts the patch based reconstruction of the target image. Such algorithms are very slow as it solves the convex optimization problem individually for each patch in the image. For example, sparse representation based image super-resolution methods are good in terms of output whilst they require high-time to perform.

In such situation, is there a method which will help to use sparse representation method for image super-resolution but it will reduce the time required by adopting the method.

Renaud Di Francesco

Have you considered transformed domain representations ? Fourier, Laplace, etc...

You could also look at signal autocorrelations eigenvalues and allocate more effort to higher eigenvalues.

You may also have a saliency notion for the type of images you consider (patterns or regions of interest) allowing to allocate resources where they matter, and make your algorithm lighter.

Have you tried sub-band analysis ? Wavelet ? These may give you more regular and predictable processing.

Hope this helps

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