Convolutional Neural Network is good at image classification. However, it is
found to be vulnerable to image quality degradation. Even a small amount of
distortion such as noise or blur can severely hamper the performance of these
CNN architectures. Most of the work in the literature strives to mitigate this
problem simply by fine-tuning a pre-trained CNN on mutually exclusive or a
union set of distorted training data. This iterative fine-tuning process with
all known types of distortion is exhaustive and the network struggles to handle
unseen distortions. In this work, we propose distortion robust DCT-Net, a
Discrete Cosine Transform based module integrated into a deep network which is
built on top of VGG16. Unlike other works in the literature, DCT-Net is "blind"
to the distortion type and level in an image both during training and testing.
As a part of the training process, the proposed DCT module discards input
information which mostly represents the contribution of high frequencies. The
DCT-Net is trained "blindly" only once and applied in generic situation without
further retraining. We also extend the idea of traditional dropout and present
a training adaptive version of the same. We evaluate our proposed method
against Gaussian blur, motion blur, salt and pepper noise, Gaussian noise and
speckle noise added to CIFAR-10/100 and ImageNet test sets. Experimental
results demonstrate that once trained, DCT-Net not only generalizes well to a
variety of unseen image distortions but also outperforms other methods in the
literature.

本文提出一种基于离散余弦变换的 DCT-Net 模块，可用于改善卷积神经网络 (CNN) 在面对包括高噪音、模糊等多种苟且不净的图像质量下的分类表现。通过丢弃高频信息，DCT-Net 能够在训练和测试过程中自适应地适应各种扭曲情况，仅需盲目一次训练即可用于未知的图像失真情况，结果表明 DCT-Net 在 CIFAR-10/100 和 ImageNet 测试数据集上具有更好的分类准确性。