论文标题
通过cahn-hilliard图像插入损坏图像预测的增强
Enhancement of damaged-image prediction through Cahn-Hilliard Image Inpainting
论文作者
论文摘要
我们评估在将受损图像传递到分类神经网络之前，在将损坏的图像传递到分类的神经网络之前评估的好处。为此，我们将修改的Cahn-Hilliard方程作为图像介入过滤器，该图像通过具有降低的计算成本和足够的能量稳定性和界限的有限体积方案来解决。这里采用的基准数据集是MNIST，它由手写数字的二进制图像组成，是验证图像处理方法的标准数据集。我们通过MNIST的训练集训练一个基于密集层的神经网络，随后我们污染了测试集，并损坏了不同类型和强度的损坏。然后，我们将神经网络的预测准确性与不应用Cahn-Hilliard滤波器进行损坏的图像测试的预测准确性。我们的结果量化了由于应用Cahn-Hilliard滤清器而导致的损坏图像预测的显着改善，对于特定的损坏可能会增加高达50％，并且对于低至中度损坏而言，这通常是有利的。
We assess the benefit of including an image inpainting filter before passing damaged images into a classification neural network. For this we employ a modified Cahn-Hilliard equation as an image inpainting filter, which is solved via a finite volume scheme with reduced computational cost and adequate properties for energy stability and boundedness. The benchmark dataset employed here is MNIST, which consists of binary images of handwritten digits and is a standard dataset to validate image-processing methodologies. We train a neural network based of dense layers with the training set of MNIST, and subsequently we contaminate the test set with damage of different types and intensities. We then compare the prediction accuracy of the neural network with and without applying the Cahn-Hilliard filter to the damaged images test. Our results quantify the significant improvement of damaged-image prediction due to applying the Cahn-Hilliard filter, which for specific damages can increase up to 50% and is in general advantageous for low to moderate damage.
