Image blur and image noise are common distortions during image acquisition. In this paper, we systematically study the effect of image distortions on the deep neural network (DNN) image classifiers. First, we examine the DNN classifier performance under four types of distortions. Second, we propose two approach es to alleviate the effect of image distortion: re-training and fine-tuning with noisy images. Our results suggest that, under certain conditions, fine -tuning with noisy images can alleviate much effect due to distorted inputs, and is more practical than re-training.
REF proposed two approaches to alleviate poor performance due to blurred and noisy images: re-training and fine-tuning with noisy images, showing that fine-tuning is more practical than re-training.
On classification of distorted images with deep convolutional neural networks
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Computer Science