Deep multi-label learning for image distortion identification

Image Distortion Identification is important for image processing system enhancement, image distortion correction and image quality assessment. Although images may suffer various number of distortions while going through different systems, most of the previous researches of image distortion identification were focus on identifying single distortion in image. In this paper, we proposed a CNN-based multi-label learning model (called MLLNet) to identify distortions for different scenarios, including images having no distortion, single distortion and multiple distortions. Concretely, we transform the multi-label classification for image distortion identification to a number of multi-class classifications and use a deep multi-task CNN model to train all associated classifiers simultaneously. For unseen image, we use the trained CNN model to predict a number of classifications at same time and fuse them to final multi-label classification. The extensive experiments demonstrate that the propose algorithm can achieve good performance on several databases. Moreover, the network architecture of the CNN model can make flexible adjustment according to the different requirements. (C) 2020 Elsevier B.V. All rights reserved.