论文标题
带有边缘的隐式神经表示和通道调整
Edge-oriented Implicit Neural Representation with Channel Tuning
论文作者
论文摘要
隐式神经表示，将图像表示为连续函数而不是离散的网格形式，被广泛用于图像处理。尽管其表现优于结果，但仍存在恢复给定信号的清晰形状（例如图像边缘）的局限性。在本文中，我们提出了梯度幅度调节算法，该算法计算了训练隐式表示的图像的梯度。此外，我们提出了面向边缘的表示网络（EOREN），可以通过拟合梯度信息（面向边缘的模块）重建图像。此外，我们添加了通道调节模块以调整给定信号的分布，从而解决了拟合梯度的慢性问题。通过分离两个模块的反向传播路径，Eoren可以学习图像的真实颜色，而不会阻碍梯度的作用。我们定性地表明，我们的模型可以重建复杂的信号，并通过定量结果证明我们的模型的一般重建能力。
Implicit neural representation, which expresses an image as a continuous function rather than a discrete grid form, is widely used for image processing. Despite its outperforming results, there are still remaining limitations on restoring clear shapes of a given signal such as the edges of an image. In this paper, we propose Gradient Magnitude Adjustment algorithm which calculates the gradient of an image for training the implicit representation. In addition, we propose Edge-oriented Representation Network (EoREN) that can reconstruct the image with clear edges by fitting gradient information (Edge-oriented module). Furthermore, we add Channel-tuning module to adjust the distribution of given signals so that it solves a chronic problem of fitting gradients. By separating backpropagation paths of the two modules, EoREN can learn true color of the image without hindering the role for gradients. We qualitatively show that our model can reconstruct complex signals and demonstrate general reconstruction ability of our model with quantitative results.
