For many years, a lot of museums and countries organize the high definition digitalization of their own collections.
In consequence, they generate massive data for each object. In this paper, we only focus on art painting
collections. Nevertheless, we faced a very large database with heterogeneous data. Indeed, image collection
includes very old and recent scans of negative photos, digital photos, multi and hyper spectral acquisitions,
X-ray acquisition, and also front, back and lateral photos. Moreover, we have noted that art paintings suffer
from much degradation: crack, softening, artifact, human damages and, overtime corruption. Considering
that, it appears necessary to develop specific approaches and methods dedicated to digital art painting analysis.
Consequently, this paper presents a complete framework to evaluate, compare and benchmark devoted to
image processing algorithms.