This disclosure relates generally to the field of image processing. More particularly, but not by way of limitation, this disclosure relates to a technique for improving image registration operations by giving more weight or significance to regions within an image deemed to be more important.
Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. The goal of image registration is to align two images—the reference and sensed images—so that when they are combined or blended together, they appear as a seamless whole (rather than as a combination of disjoint images). One approach, known as feature-based registration, seeks to identify unique features in both the reference and sensed images (e.g., edges, line endings, centers of gravity and the like). The correspondence between the two sets of detected features then drives image alignment.
During image registration, the use of foreground imagery versus background imagery can produce different results, where the selection of one can lead to visually poor results. This problem can arise, for example, because of parallax. Consider the capture of an individual's portrait using a multi-image capture technique such as high dynamic range (HDR) imaging. In such cases, the individual is most often close to the camera while the background is far away (e.g., a tree line). Here, a small camera motion will cause the individual's face to move in relation to the edge of the frame more than the background tree-line. Unfortunately, trees can provide a stronger signature for registration than would the individual's face. (The same is true for any background having a large number of detectable edges, line endings and the like compared to the foreground subject.) Automatic feature-based registration techniques would use the background for registration purposes and, as a result, ghosting of the foreground subject (e.g., the individual's face) would exhibit ghosting.