Partial closing filters for image restoration

Stephen S. Wilson

doi:10.1117/12.205216

28 March 1995 Partial closing filters for image restoration

Stephen S. Wilson

Proceedings Volume 2424, Nonlinear Image Processing VI; (1995) https://doi.org/10.1117/12.205216
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States

Abstract

A partial closing is class of edge preserving operators where a dilation-like operation is followed by an erosion with a convex structuring element. These operators are increasing, and edge preserving, but in general satisfy none of the other formal properties of the standard morphological closing which is a special case of this operator. The purpose of the partial closing is the restoration of certain classes of images by filling in gaps caused by noise. However, the examples and analysis to be given involves one dimensional images. The method can be applied to two dimensional images that are comprised of short line segments that occur for example in character strokes or image edges after an edge detection operation. One type of partial closing is an order statistic filter followed by an erosion. Another type-a dilation partial closing is a dilation with a sparse structuring element followed by an erosion with a convex structuring element. Dilation partial closings exist that are excellent approximations to the median filters with sliding windows of diameters 3, 5, and 7. The use of dilation partial closings in place of the median filters results in a considerable savings in computer time. The statistics of the partial closings are independent of the threshold. Thus the filters can be generalized to gray levels using stack filters. The dilation partial filters are then expressed in terms of minima and maxima.

Citation Download Citation

Stephen S. Wilson "Partial closing filters for image restoration", Proc. SPIE 2424, Nonlinear Image Processing VI, (28 March 1995); https://doi.org/10.1117/12.205216

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KEYWORDS

Digital filtering

Image segmentation

Interference (communication)

Image filtering

Electronic filtering

Statistical analysis

Image restoration

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