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.

翻译：尽管深度神经网络在图像复原任务中表现出极强的能力，但其仍存在若干局限性。这些网络的工作原理尚不明确，且易受训练集固有偏差的强烈影响。解决这些缺陷的一种途径是对训练集实现更优的控制，特别是通过使用合成数据集。本文提出一种基于若干简明原理的合成图像生成器。我们重点关注几何建模、纹理特性以及简化的图像采集建模。将这些特性整合到经典的枯叶模型中，能够构建高效的训练数据集。标准的图像去噪与超分辨率网络可在此类数据集上进行训练，其性能表现接近在自然图像数据集上训练的结果。作为可解释性研究的初步探索，我们对所采用的原理进行了细致分析，明确了哪些图像特性是获得优异性能所必需的。此外，此类训练还能提升模型对测试集各类几何与辐射度扰动的鲁棒性。