The present paper gives a new formulation for rough surface scattering in terms of a stochastic integral equation which can be dealt with by means of stochastic functional approach. The random surface is assumed to be infinite and a homogeneous Gaussian random process. The random wave field is represented in the stochastic Floquet form due to the homogeneity of the surface, and in the non-Rayleigh form consisting of both upward and downward going scattered waves, as well as in the extended Voronovich form based on the consideration of the level-shift invariance. The stochastic integral equations of the first and the second kind are derived for the unknown surface source function which is a functional of the derivative or the increment of the surface profile function. It is also shown that the inhomogeneous term of the stochastic integral equation of the second kind automatically gives the solution of the Kirchhoff approximation for infinite surface.

A new type of noncausal stochastic model is proposed to represent a random image with double peak spectrum. The model based on the assumption that the double peak spectrum is expressed by a product of two spectra located at two symmetric positions in the 2D spatial frequency space. Estimation of model parameters is made by means of minimizing the "whiteness" which was proposed in authors' previous work. In a simulation for model estimation we make use of computer-generated random images with double peak spectrum. Comparing this with the estimation by a causal model, we demonstrate that the present method can better estimate not only the spectral peak location but also the spectral shape. The proposed model can be extend to an image model with multl-peak spectrum. However, Increase of parameters makes the model estimation more difficult We try a model with triple peak spectra since a real texture image usually possesses a spectral peak at the origin besides the two peaks. A result shows that the estimation of three spectral positions are good enough, but their spectral shapes are not necessarily satisfactory. It is expected that the estimation of multi-peaked spectral model can be made better by improving the process of minimizing the "whiteness."