Texture Representation and Synthesis Using
Correlation of Complex Wavelet Coefficient Magnitudes
Published as:
Technical Report #54,
Consejo Superior de Investigaciones Cientificas (CSIC), Madrid.
29 March 1999.
We present a statistical characterization of texture images in the
context of an overcomplete complex wavelet transform. The
characterization is based on empirical observations of statistical
regularities in such images, and parameterized by (1) the local
auto-correlation of the coefficients in each subband; (2) both the
local auto-correlation and cross-correlation of coefficient {\em
magnitudes} at other orientations and spatial scales; and (3) the
first few moments of the image pixel histogram. We develop an
efficient algorithm for synthesizing random images subject to these
constraints using alternated projections, and demonstrate its
effectiveness on a wide range of synthetic and natural textures. We
also show the flexibility of the representation, by applying to a
variety of tasks which can be viewed as constrained image synthesis
problems.
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