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Extra resources for Image and video databases: restoration, watermarking, and retrieval

Example text

There are many solutions to a restoration problem that give identical observed signals when the degradation model (though be it with different parameters) is applied to them. For example, image data corrupted by blotches can be restored by a number of methods (Chapter 4), each of which gives a different solution. However, none of the solutions conflict with the degradation process and with the observed data that result from the degradation process. The restoration problem is ill posed in the sense that no unique inverse to the degradation exists.

Many motion estimators, including the hierarchical motion estimator used in this book, assume the constant luminance constraint [Tek95]. This constraint, which requires that there be no variations in luminance between consecutive frames, is not met in the presence of intensity flicker. 6d shows the MSE computed for the three test sequences to which varying amounts of intensity flicker have been added. 6a-c. In conclusion, artifacts can have a considerable impact on the accuracy of estimated motion vectors.

Because these estimates will never be perfect, the effects of errors in a ( i ) and b(i) on y(t) is investigated. To simplify the analysis, the influence of noise is discarded. For ease of notation, the following analysis leaves out the spatial and temporal indices. Let a = a + Da and b - b + Db. 1 plots the reconstruction error as a function of Da and Db with a = 1, b = 0 and y=100. 7) shows that Ay is much more sensitive to errors in a ( i ) than to errors in b ( i ) . It also shows that the sensitivity due to errors Act can be minimized in absolute terms by centering the range of image intensities around 0.

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