Impact of quality distortions on CNN image classifiers and restoration using deep convolutional auto-encoder

Abstract

Deep neural networks have obtained significant performance on recognizing objects even in real-time video stream. As hardware requirements of this task decrease in cost and parts getting smaller, this technology can be used in mobile devices Most of the time, deep neural networks are trained and tested on quality picture datasets. But frames provided by camera may be distorted because of camera defects and/or weather conditions such as rain and snow. This paper provides an evaluation of four state-of-the-art deep neural network models for picture classification under quality distortions. Three types of quality distortions are considered: blur, noise and contrast. It is shown that the existing networks are susceptible to these quality distortions and architecture of the network dramatically affects the results. Using deep convolutional auto-encoder to restore picture quality is suggested and better scores have been archived utilizing it. Results enable future work in developing machine vision systems on that are more invariant to quality distortions.