Carrillo José A, Kalliadasis Serafim, Liang Fuyue, Perez Sergio P
Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK.
Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK.
R Soc Open Sci. 2021 May 19;8(5):201294. doi: 10.1098/rsos.201294.
We assess the benefit of including an image inpainting filter before passing damaged images into a classification neural network. We employ an appropriately modified Cahn-Hilliard equation as an image inpainting filter which is solved numerically with a finite-volume scheme exhibiting reduced computational cost and the properties of energy stability and boundedness. The benchmark dataset employed is Modified National Institute of Standards and Technology (MNIST) dataset, which consists of binary images of handwritten digits and is a standard dataset to validate image-processing methodologies. We train a neural network based on dense layers with MNIST, and subsequently we contaminate the test set with damages 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 by applying the Cahn-Hilliard filter, which for specific damages can increase up to 50% and is advantageous for low to moderate damage.
我们评估了在将受损图像输入分类神经网络之前加入图像修复滤波器的益处。我们采用经过适当修改的Cahn-Hilliard方程作为图像修复滤波器，并用有限体积格式进行数值求解，该格式具有降低的计算成本以及能量稳定性和有界性的特性。所使用的基准数据集是修改后的美国国家标准与技术研究院（MNIST）数据集，它由手写数字的二值图像组成，是验证图像处理方法的标准数据集。我们基于密集层用MNIST训练一个神经网络，随后用不同类型和强度的损伤对测试集进行污染。然后，我们比较了对受损图像测试应用和不应用Cahn-Hilliard滤波器时神经网络的预测准确率。我们的结果量化了应用Cahn-Hilliard滤波器对受损图像预测的显著改善，对于特定损伤，预测准确率可提高高达50%，并且对低到中等程度的损伤具有优势。
