In this procedure, the images to be splined are first decomposed into a set of band-pass filtered component images. Next, the component images in each spatial frequency band are assembled into a corresponding band-pass mosaic. In this step, component images are joined using a weighted average within a transition zone which is proportional in size to the wave lengths represented in the band. Finally, these band-pass mosaic images are summed to obtain the desired image mosaic.
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On the other hand, to avoid a double exposure effect, the zone should not be much larger than the smallest prominent image features. There is no choice of T which satisfies both requirements in the star images of Figure 3 because these contain both a diffuse background and small bright stars.
In particular, a suitable T can only be selected if the images to be splined occupy a relatively narrow spatial frequency band. A separate spline with an appropriately selected T can then be performed in each band. Finally, the splined band-pass components are recombined into the desired mosaic image.
We call this approach the multiresolution spline. Basic Pyramid Operations A sequence of low-pass filtered images Go, G1 … , GN can be obtained by repeatedly convolving a small weighting function with an image Convolution with a Gaussian has the effect of low-pass filtering the image. Pyramid construction is equivalent to convolving the image with a set of Gaussian-like functions to produce a corresponding set of filtered images.
Because of the importance of the multiple filter interpretation, we shall refer to this sequence of images Go,G1 … GN as the Gaussian pyramid.
The Gaussian pyramid is a set of low-pass filtered images. In order to obtain the band-pass images required for the multiresolution spline we subtract each level of the pyramid from the next lowest level. This difference of Gaussian-like functions resembles the Laplacian operators commonly used in the image processing , so we refer to the sequence Lo, L1 … , LN as the Laplacian pyramid.
A multiresolution spline with application to image mosaics
Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. Burt and E. Adelson Fig. A pair of images may be represented as a pair of surfaces above the x, y plane. The problem of image splining is to join these surfaces with a smooth seam, with as little distortion of each surface as possible.
图像拼接--A multiresolution spline with application to image mosaics