  An adaptive optimization technique to improve precision of quantum homodyne
tomography is presented. The method is based on the existence of so-called null
functions, which have zero average for arbitrary state of radiation. Addition
of null functions to the tomographic kernels does not affect their mean values,
but changes statistical errors, which can then be reduced by an optimization
method that "adapts" kernels to homodyne data. Applications to tomography of
the density matrix and other relevant field-observables are studied in detail.
