VibrantLeaves: A principled parametric image generator for training deep restoration models
Journal:
arXiv
Published Date:
Apr 14, 2025
Abstract
Even though Deep Neural Networks are extremely powerful for image restoration
tasks, they have several limitations. They are poorly understood and suffer
from strong biases inherited from the training sets. One way to address these
shortcomings is to have a better control over the training sets, in particular
by using synthetic sets. In this paper, we propose a synthetic image generator
relying on a few simple principles. In particular, we focus on geometric
modeling, textures, and a simple modeling of image acquisition. These
properties, integrated in a classical Dead Leaves model, enable the creation of
efficient training sets. Standard image denoising and super-resolution networks
can be trained on such datasets, reaching performance almost on par with
training on natural image datasets. As a first step towards explainability, we
provide a careful analysis of the considered principles, identifying which
image properties are necessary to obtain good performances. Besides, such
training also yields better robustness to various geometric and radiometric
perturbations of the test sets.
