An adaptive algorithm that implements a saturation-based ordering
(for color image morphology) is introduced. The adaptation is
achieved using a tradeoff parameter in the form of a nonlinear function of
the local saturation. To evaluate the performance of the proposed algorithm,
a deigned psychophysical experiment is used to derive a metric
denoted as the average value for the psychophysical evaluation in percent
(APE%). Results of implementing the proposed APE show that an
APE573 to 96% can be achieved for basic morphological operators, i.e.,
dilation, erosion, opening, and closing. APE value depends on the size
and shape of the structuring element as well as on the image details.
The proposed algorithm has also been extended to other morphological
operators, such as image smoothing (noise suppression), top hat, gradient,
and Laplacian operators. In the case of a smoothing operation, an
average peak signal-to-noise ratio (PSNR)531 to 37 dB is achieved at
various structuring elements and applied noise variances, while good
results are achieved with the proposed top-hat operators.