Robustness Of Deep Convolutional Neural Networks For Image Degradations

Deep convolutional neural networks (CNNs) have achieved tremendous success in image recognition tasks. However, the performance of CNNs degrade in situations where the input image is degraded by compression artifacts, blur or noise. In this paper, we analyze some of the common CNNs for degradations in images caused by Gaussian noise, blur as well as compression using JPEG and JPEG 2000 for the full range of quality factors. Moreover, we propose a method to improve the performance of CNNs for image classification in the presence of input images with degradations based on a master-slave architecture. Our method was found to perform well for individual and combined degradations.