Coupled Map Lattice (CML) usually serves as a pseudo-random number generator for encrypting digital images. Based on our analysis, the existing CML-based systems still suffer from problems like limited parameter space and local chaotic behavior. In this paper, we propose a novel intermittent jumping CML system based on multiple chaotic maps. The intermittent jumping mechanism seeks to incorporate the multi-chaos, and to dynamically switch coupling states and coupling relations, varying with spatiotemporal indices. Extensive numerical simulations and comparative studies demonstrate that, compared with the existing CML-based systems, the proposed system has a larger parameter space, better chaotic behavior, and comparable computational complexity. These results highlight the potential of our proposal for deployment into an image cryptosystem.

With the stimulating demand of consumers for personalized garments, mass customization is gradually becoming prevalent. However, the automatic generation of personalized garment patterns remains core to mass customization. In this article, we propose a novel parametric apparel pattern-making method and achieve the automatic generation of personalized garment patterns in batches. First, we review the basic principle of biarc and present a new parametric pattern-making method based on biarc and ezdxf. Second, 1000 3D human models randomly generated by the SMPL-X parametric human model are clustered into 10 classes with the k-means clustering method, and 10 representative human models are selected from each cluster to generate their personalized garment pattern. Finally, the rationality of the personalized garment pattern is verified by virtual fitting. Our method has several advantages: (1) the proposed parametric biarc can be easily used to build parametric garment patterns without being limited by the style of the garment, (2) the proposed method is capable of generating personalized garment patterns in batches by imputing measurements from large numbers of individuals, and it only takes very little time, and (3) the personalized garment patterns can fit human bodies very well. The proposed method can be used to build parametric garment patterns and to achieve the batch generation of personalized patterns, improving the efficiency of garment customization and the quality of final products.

In this paper, we propose a novel spatial image error concealment (EC) method based on deep neural network. Considering that the natural images have local correlation and non-local self-similarity, we use the local information to predict the missing pixels and the non-local information to correct the predictions. The deep neural network we utilize can be divided into two parts: the prediction part and the auto-encoder (AE) part. The first part utilizes the local correlation among pixels to predict the missing ones. The second part extracts image features, which are used to collect similar samples from the whole image. In addition, a novel adaptive scan order based on the joint credibility of the support area and reconstruction is also proposed to alleviate the error propagation problem. The experimental results show that the proposed method can reconstruct corrupted images effectively and outperform the compared state-of-the-art methods in terms of objective and perceptual metrics.