论文标题
部分可观测时空混沌系统的无模型预测
Jointly Resampling and Reconstructing Corrupted Images for Image Classification using Frequency-Selective Mesh-to-Grid Resampling
论文作者
论文摘要
神经网络在过去几年中成为图像分类的标准技术。他们正在训练阶段从大量图像中提取图像特征。在接下来的测试阶段，将网络应用于训练的问题并测量其性能。在本文中，我们专注于图像分类。神经网络解释的视觉数据量随着神经网络的使用量的增加而增长。通常，视觉数据从应用程序端传输到进行解释的中央服务器。如果传输受到干扰，则会在传输图像中发生损失。这些损失必须使用后处理重建。在本文中，我们结合了广泛应用的双线性和双色插值以及高质量的重建频率选择性重建（FSR），以重建损坏的图像。但是，我们建议将频率选择性的网格到网格重采样（FSMR）进行损坏的图像的关节重建和调整。研究了EditigyNetB0，Densenet121，Densenet201，Resnet50和Resnet152的分类精度的性能。结果表明，使用FSMR的重建导致大多数网络的分类精度最高。 Densenet121的平均改善最高为6.7个百分点。
Neural networks became the standard technique for image classification throughout the last years. They are extracting image features from a large number of images in a training phase. In a following test phase, the network is applied to the problem it was trained for and its performance is measured. In this paper, we focus on image classification. The amount of visual data that is interpreted by neural networks grows with the increasing usage of neural networks. Mostly, the visual data is transmitted from the application side to a central server where the interpretation is conducted. If the transmission is disturbed, losses occur in the transmitted images. These losses have to be reconstructed using postprocessing. In this paper, we incorporate the widely applied bilinear and bicubic interpolation and the high-quality reconstruction Frequency-Selective Reconstruction (FSR) for the reconstruction of corrupted images. However, we propose to use Frequency-Selective Mesh-to-Grid Resampling (FSMR) for the joint reconstruction and resizing of corrupted images. The performance in terms of classification accuracy of EfficientNetB0, DenseNet121, DenseNet201, ResNet50 and ResNet152 is examined. Results show that the reconstruction with FSMR leads to the highest classification accuracy for most networks. Average improvements of up to 6.7 percentage points are possible for DenseNet121.
