3 ms·

Ah, that's incredibly clever, but that must be incredibly expensive. For every pixel window you're trying to resolve, you have to look at (and generate smaller

• jacobolus 14y ago
  The full PDF explaining their method is linked at the top of the page. http://www.wisdom.weizmann.ac.il/~vision/single_image_SR/files/single_image_SR.pdf http://www.wisdom.weizmann.ac.il/~vision/single_image_SR/fil... [...] there is plenty of patch redundancy within a single image L. Let p be a pixel in L, and P be its surrounding patch (e.g., 5 × 5), then there exist multiple similar patches P1,...Pk in L (inevitably, at sub-pixel shifts). These patches can be treated as if taken from k different low-resolution images of the same high resolution “scene”, thus inducing k times more linear constraints (Eq. (1)) on the high-resolution intensities of pixels within the neighborhood of q ∈ H (see Fig. 3b). For increased numerical stability, each equation induced by a patch Pi is globally scaled by the degree of similarity of Pi to its source patch P. [...] [...] Assuming sufficient neighbors are found, this process results in a determined set of linear equations on the unknown pixel values in H. Globally scale each equation by its reliability (determined by its patch similarity score), and solve the linear set of equations to obtain H. Then they also add patches at different scale by making a few more versions of the image which have been scaled down by various amounts, and doing comparisons between the target patch and patches in those images too. If good matches are found, they can then use the original higher-resolution original patch that yielded the match when it was shrunk.