无需参数调优，快速分解图像结构与纹理
Parameter-free structure-texture image decomposition by unrolling
- 通过展开低块秩模型构建神经网络，自动学习参数
- 计算速度更快，效果接近传统迭代方法
- 在自然图像上表现良好，适合实际图像处理
本文提出一种无需参数调优且高效的结构-纹理图像分解方法。我们基于低块秩模型的展开构建了LPR-NET神经网络，既能从数据中自动学习参数，又在计算效率上优于传统迭代模型方法，同时保持相近的定性效果。尽管网络仅在合成图像上训练，数值实验表明其在自然图像上仍具备良好的泛化能力。
原文摘要 · Abstract (English)
In this work, we propose a parameter-free and efficient method to tackle the structure-texture image decomposition problem. In particular, we present a neural network LPR-NET based on the unrolling of the Low Patch Rank model. On the one hand, this allows us to automatically learn parameters from data, and on the other hand to be computationally faster while obtaining qualitatively similar results compared to traditional iterative model-based methods. Moreover, despite being trained on synthetic images, numerical experiments show the ability of our network to generalize well when applied to natural images.
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