Image De-Quantization Using Generative Models as Priors
Abstract
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.
Cite
@article{arxiv.2007.07923,
title = {Image De-Quantization Using Generative Models as Priors},
author = {Kalliopi Basioti and George V. Moustakides},
journal= {arXiv preprint arXiv:2007.07923},
year = {2020}
}