Secure separable reversible data hiding of halftone image in blockchain

To realize the distributed storage and management of a secret halftone image in blockchain,a secure separable reversible data hiding(RDH)of halftone image in blockchain(SSRDHB)was proposed.A secret halftone image can be used as the original image to generate multiple share images which can be distributed storage in each point of blockchain,and additional data can be hidden to achieve management of each share image.Firstly,the secret halftone image was encrypted through Zu Chongzhi(ZUC)algorithm by using the encryption key(EK).Secondly,the method of using odd or even of share data was proposed to hide data,and a share dataset can be generated by using polynomial operation.Thirdly,multiple share images can be obtained through selecting share data,and different additional data can be hidden through controlling odd or even of share data,and additional data can be protected by using data-hiding key(DK).After sharing process,if the receiver has both keys,the halftone image can be recovered and additional data can be revealed,and two processes are separable.Experiment results show that multiple share images hidden additional data can be obtained through SSRDHB,and the halftone image can be recovered with 100%by picking any part of share images,and one additional data can be revealed with 100%by picking any one share image.

CMAC Based Color Separation in Printing Images

To over come the drawbacks existing in current measurement methods for detecting and controlling colors in printing process, a new medal for color separation and dot recognition is proposed from a view of digital image processing and patter recognition. In this model, firstly data samples are collected from some color patches by the Fuzzy C-Means （FCM） method; then a classifier based on the Cerebellar Model Articulation Controller （CMAC） is constructed which is used to recognize color pattern of each pixel in a microscopic halftone image. The principle of color separation and the algorithm model are introduced and the experiments show the effectiveness of the CMAC-based classifier as opposed to the BP network.

An improved parallel contrast-aware halftoning

Digital image halftoning is a widely used technique. However, achieving high fidelity tone reproduction and structural preservation with low computational time cost remains a challenging problem. This paper presents a highly parallel algorithm to boost real-time application of serial structure-preserving error diffusion. The contrast-aware halftoning approach is one such technique with superior structure preservation, but it offers only a limited opportunity for graphics processing unit(GPU) acceleration. Our method integrates contrast-aware halftoning into a new parallelizable error-diffusion halftoning framework. To eliminate visually disturbing artifacts resulting from parallelization, we propose a novel multiple quantization model and space-filling curve to maintain tone consistency, blue-noise property, and structure consistency. Our GPU implementation on a commodity personal computer achieves a real-time performance for a moderately sized image. We demonstrate the high quality and performance of the proposed approach with a variety of examples, and provide comparisons with state-of-the-art methods.