论文标题
学习图像denoising的整合模型
Learning Integrodifferential Models for Image Denoising
论文作者
论文摘要
我们引入了用于图像denoising的边缘增强各向异性扩散模型的整合分化扩展。通过在多个尺度上积累加权结构信息，我们的模型是第一个通过多尺度集成创建各向异性的模型。它遵循了将基于模型和数据驱动方法的优势结合在紧凑，有见地和数学上有充分根据的模型中的优势，并提高了性能。我们探索了训练有素的自适应加权和对比度参数的训练结果，以通过平滑功能获得明确的建模。这导致了一个仅具有三个参数的透明模型，而没有显着降低其非降低性能。实验表明，它的表现优于基于扩散的前辈。我们表明，多尺度信息和各向异性对其成功至关重要。
We introduce an integrodifferential extension of the edge-enhancing anisotropic diffusion model for image denoising. By accumulating weighted structural information on multiple scales, our model is the first to create anisotropy through multiscale integration. It follows the philosophy of combining the advantages of model-based and data-driven approaches within compact, insightful, and mathematically well-founded models with improved performance. We explore trained results of scale-adaptive weighting and contrast parameters to obtain an explicit modelling by smooth functions. This leads to a transparent model with only three parameters, without significantly decreasing its denoising performance. Experiments demonstrate that it outperforms its diffusion-based predecessors. We show that both multiscale information and anisotropy are crucial for its success.
