Super resolution in matlab

Super resolution In matlab

Single-image super-resolution refers to the task of constructing a high-resolution enlargement of a given low-resolution image. Usual interpolation-based magnification introduces blurring. Then, the problem cast into estimating missing high-frequency details. Based on the framework of Freeman et al. [1], we investigate a regression-based approach. The system consists of four components:

1.       interpolation of the input low-resolution image into the desired scale

2.       generation of a set of candidate images based on patch-wise regression: kernel ridge regression is utilized; To reduce the time complexity (around 200,000 data points), a sparse basis is found by combining kernel matching pursuit and gradient descent

3.       combining candidates to produce an image: patch-wise regression of output results in a set of candidates for each pixel location; An image output is obtained by combining the candidates based on estimated confidences for each pixel.

4.       post-processing based on the discontinuity prior of images: as a regularization method, kernel ridge regression tends to smooth major edges; The natural image prior proposed by Tappen et al. [2] is utilized to post-process the regression result such that the discontinuity at major edges are preserved.

: Overview of super-resolution shown with an example: (a) input image is interpolated into the desired scale, (b) a set of candidate images is generated as the result of regression, (c) candidates are combined based on estimated confidences; The combined result is sharper and less noisy than individual candidates, which however shows ringing artifacts, and (d) post-processing removes ringing artifacts and further enhances edges.

Sample Implementation

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