Deep neural network for halftone image classification based on sparse auto-encoder

To restore high quality continuous tone images from each class of halftone images, halftone image fine classification is the key problem. In this paper, a novel feature learning method is proposed for classifying 14 kinds of halftone images produced by the most well-known halftoning algorithms. This study employs the stacked sparse auto-encoders (SAE) trained with unsupervised learning for extracting features of halftone images, and then uses softmax regression with supervised learning for fine-tuning the deep neural network and classifying halftone images. In order to reduce the run-time of deep neural network and improve the image correct classification rate, we propose an effective patch extraction method for testing halftone images by measuring the mean and variance of local entropy in a patch. Halftone image classification is determined by the classification results of all effective patches inside an image via majority voting (MV). The experimental results demonstrate that our proposed method achieves an average correct classification rate (ACCR) of over 99.44% for 14 kinds of halftone images on two public image sets. Compared with state-of-the-art LMS-Bayes and M10-ML methods, the proposed SAE-MV method can distinguish the most categories of halftone images and achieve competitive ACCR, meanwhile, demonstrate better generalization performance.