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Abstract

A system for identifying and tagging anomalies, such as images of dust and scratches, in a digital image so that they can be removed from the image. The invention implements a technique for detecting anomalies designed to minimize the number of false positives, i.e., non-anomalous portions of the image falsely identified as anomalies. The technique bases the initial first-pass identification of anomalous image regions on the difference between the gradient of the image at each of a set of grid points in the image and the mean of the gradient of the image at nearby points. Thus a region is identified as anomalous if original image pixel values in its immediate neighborhood are more variable than they are in a larger neighborhood. This technique greatly reduces (when compared to the prior art) the identification of false positives in noisy regions of the image, such as the leaves of a tree in daylight or a pebbly beach. After a first-pass list of candidate anomalous regions has been made, the invention culls the list by means of a set of heuristic measures, including but not limited to a set of shape, size, color and visibility measures, designed to indicate how much a candidate resembled a dust fragment or a scratch.

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First Claim