  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.
