Restoration of scanning tunneling microscope images by means of two-dimensional maximum entropy method

We present a new technique for the restoration of scanning tunneling microscopy (STM) images, which is a two-dimensional extension of a recently developed statistical approach based on the one-dimensional least-squares method (LSM). An STM image is regarded as a realization of a stochastic process and assumed to be a composition of an underlying image and noise. We express the underlying image in terms of a two-dimensional generalized trigonometric polynomial suitable for representing the atomic protrusions in STM images. The optimization of the polynomial is performed by the two-dimensional LSM combined with the power spectral density function estimated by means of the maximum entropy method (MEM) iterative algorithm for two-dimensional signals. The restored images are obtained as the optimum least-squares fitting polynomial which is a continuous surface. We apply this technique to modeled and actual STM data. Results show that the present method yields a reasonable restoration of STM images.