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From: aramini@apollo.COM (Michael Aramini)
Newsgroups: comp.graphics
Subject: Re: Image Processing question
Message-ID: <3f4b209f.c463@apollo.COM>
Date: 26 Oct 88 20:21:00 GMT
Article-I.D.: apollo.3f4b209f.c463
References: <26451@ucbvax.BERKELEY.EDU> <62300003@urbsdc> <3f4a9a6e.c463@apollo.COM>
Organization: Apollo Computer, Chelmsford, Mass.
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                                       In addition it gives a cubic spline
    interpolation weighting function not usually discussed in textbooks (i.e. it is
    *not* the traditional cubic B-spline) which gives good results with medicalCT
    and MRI images:

        Parker, J. A., R. V. Kenyon, and Troxel, D. E., "Comparison of Interpolating
          Methods for Image Resampling", _IEEE Transations on Medical Systems_, Vol.
          MI-2, No. 1, March, 1983, pp. 31-39.

At the risk of being reduntant, let me elaborate on this.  The traditional cubic
B-spline interpolation function smoothes the data, i.e. if you used it to
resample the image for the same data points as the input, you would get a blurred
version of the orginal image.  This blurring can be useful if the input image
is noisy.  However, for many applications it smoothes the data too much.

The so called high resolution cubic spline interpolation function described in the
paper does not smooth the data nearly as much. In fact if you used it to
resample the image for the same data points as the input, you would get the
original image back.  In general, it results in clearer interpolated images,
and tends to enhance contrast.  However, one thing to be aware of is that
the output image may have pixel values outside the range of the input pixel
values, which can cause underflows and overflows, so you have check for such
conditions and set the output pixel value to the bottom or top of the allowed
range of pixel values when appropriate.

-Michael