The Weizmann Institute of Science
Faculty of Mathematics and Computer Science
Computer Vision Lab

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Abstract

Statistics of ‘natural images’ provides useful priors for solving under-constrained problems in Computer Vision. Such statistics is usually obtained from large collections of natural images. We claim that the substantial internal data redundancy within a single natural image (e.g., recurrence of small image patches), gives rise to powerful internal statistics, obtained directly from the image itself. While internal patch recurrence has been used in various applications, we provide a parametric quantification of this property. We show that the likelihood of an image patch to recur at another image location can be expressed parametricly as a function of the spatial distance from the patch, and its gradient content. This “internal parametric prior” is used to improve existing algorithms that rely on patch recurrence. Moreover, we show that internal image-specific statistics is often more powerful than general external statistics, giving rise to more powerful image-specific priors. In particular:
(i) Patches tend to recur much more frequently (densely) inside the same image, than in any random external collection of natural images.
(ii) To find an equally good external representative patch for all the patches of an image, requires an external database of hundreds of natural images.
(iii) Internal statistics often has stronger predictive power than external statistics, indicating that it may potentially give rise to more powerful image-specific priors.

This web page contains:

1. More examples of External vs. Internal super-resolution (Sec. 4).
2. Elaborating on the parametric approximation of the NN function (Sec. 2).