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Estimation of the noncausal image model described by a symmetric spatial filter is important in the study of statistical image processing. However, the estimating methods proposed so far have some problems in that they require too much computation and are not necessarily good enough to estimate a white-noise-driven image model. To cope with such difficulties, this paper proposes a new method for noncausal model estimation where a test function is introduced called ‘whiteness’ which represents the degree of spectral whiteness of an image field. ‘Whiteness’ is a positive-valued functional of the sample correlation function and takes the minimum value 0 when the image is whitened. Thus, by minimizing the ‘whiteness’ by some optimization algorithm, the parameters of the whitening filter are estimated along with the support on which the model parameter is located. This method is applied successfully to various images to demonstrate its effectiveness; and three examples, i.e., two simulated images with single- and double-peaked spectra and a texture pattern, are treated by the proposed method. For the simulated images, original correlation functions and spectra are reproduced; and, for the texture image, an almost perfect whitening filter is obtained. The present method is shown to be more effective than the conventional most likelihood method based on AIC or BIC, not only in parameter estimation but also in determining the size of support of a noncausal image model. [ABSTRACT FROM AUTHOR]