  Image quantization is used in several applications aiming in reducing the
number of available colors in an image and therefore its size. De-quantization
is the task of reversing the quantization effect and recovering the original
multi-chromatic level image. Existing techniques achieve de-quantization by
imposing suitable constraints on the ideal image in order to make the recovery
problem feasible since it is otherwise ill-posed. Our goal in this work is to
develop a de-quantization mechanism through a rigorous mathematical analysis
which is based on the classical statistical estimation theory. In this effort
we incorporate generative modeling of the ideal image as a suitable prior
information. The resulting technique is simple and capable of de-quantizing
successfully images that have experienced severe quantization effects.
Interestingly, our method can recover images even if the quantization process
is not exactly known and contains unknown parameters.
