  Image and texture synthesis is a challenging task that has long been drawing
attention in the fields of image processing, graphics, and machine learning.
This problem consists of modelling the desired type of images, either through
training examples or via a parametric modeling, and then generating images that
belong to the same statistical origin.
  This work addresses the image synthesis task, focusing on two specific
families of images -- handwritten digits and face images. This paper offers two
main contributions. First, we suggest a simple and intuitive algorithm capable
of generating such images in a unified way. The proposed approach taken is
pyramidal, consisting of upscaling and refining the estimated image several
times. For each upscaling stage, the algorithm randomly draws small patches
from a patch database, and merges these to form a coherent and novel image with
high visual quality. The second contribution is a general framework for the
evaluation of the generation performance, which combines three aspects: the
likelihood, the originality and the spread of the synthesized images. We assess
the proposed synthesis scheme and show that the results are similar in nature,
and yet different from the ones found in the training set, suggesting that true
synthesis effect has been obtained.
