Towards High-Resolution Copy-Evident Ceramic Tiles: A Deep Learning Framework for Halftoning and Watermarking

Ceramic art is essential in interior design and decoration, and making exquisite ceramic tiles imposes strict requirements for inkjet printing technology. High-resolution ceramic tiles are often produced through inkjet printing, in which the input images are converted into a halftone format. However, traditional binary halftoning techniques cannot produce high-resolution images for the ensuing printing process. Given that the processes of inkjet printing and high-temperature firing of ceramic tiles are a highly complex nonlinear system, and existing halftoning methods pose intractable problems, including inconsistent textures and color deviations. Based on a modified U-Net model and a modified error diffusion algorithm, we propose a multilevel halftoning method, which is capable of converting color-separation images of ceramic tiles into high-resolution halftone images. To deter copyright infringement, we further apply an ad hoc invisible watermarking method for halftone images. In this paper, we propose a four-stage framework: (1) A self-built dataset is used to solve non-convergence and overfitting problems caused by the unbalanced samples and non-uniqueness of halftone images. (2) A modified U-Net model is trained on the self-built dataset and applied to the ceramic-tile images. (3) An improved error diffusion algorithm is used to calibrate and convert the predicted continuous-tone transition images into multilevel halftone images for inkjet printing. (4) A invisible and robust watermark is embedding algorithm towards halftone images is proposed for copyright protection. Experimental results show that our methodology is effective for performing the color-to-halftone transformation and identifying the copyright.

A Novel Raster Map Exchange Scheme Based on Visual Cryptography

Raster map is an image that has been discretized in space and brightness, and it is an important carrier of geospatial data. With the rapid development of Internet and big data technologies, preserving the privacy of raster map has become an urgent task. To solve these issues, we propose a novel extended visual cryptography scheme to securely store a raster map into other two meaningful halftone maps in the paper. The scheme avoids the random-looking shares of visual cryptography schemes which are vulnerable and hard to manage. We first apply the halftone and color decomposition methods to transform a color secret map into halftone images. After that, we encode the secret map block by block to avoid pixel expansion. At last, by optimizing the selection of encrypted blocks, we achieve a high-quality secret recovery from generated multiple equal-sized shares. The technique used is to employ a versatile and secure raster map exchange. Experimental results show that, compared with previous work, the proposed scheme significantly improves the performance of recovered raster maps.

An Efficient Convolutional Neural Network Model Combined with Attention Mechanism for Inverse Halftoning

Inverse halftoning acting as a special image restoration problem is an ill-posed problem. Although it has been studied in the last several decades, the existing solutions can’t restore fine details and texture accurately from halftone images. Recently, the attention mechanism has shown its powerful effects in many fields, such as image processing, pattern recognition and computer vision. However, it has not yet been used in inverse halftoning. To better solve the problem of detail restoration of inverse halftoning, this paper proposes a simple yet effective deep learning model combined with the attention mechanism, which can better guide the network to remove noise dot-patterns and restore image details, and improve the network adaptation ability. The whole model is designed in an end-to-end manner, including feature extraction stage and reconstruction stage. In the feature extraction stage, halftone image features are extracted and halftone noises are removed. The reconstruction stage is employed to restore continuous-tone images by fusing the feature information extracted in the first stage and the output of the residual channel attention block. In this stage, the attention block is firstly introduced to the field of inverse halftoning, which can make the network focus on informative features and further enhance the discriminative ability of the network. In addition, a multi-stage loss function is proposed to accelerate the network optimization, which is conducive to better reconstruction of the global image. To demonstrate the generalization performance of the network for different types of halftone images, the experiment results confirm that the network can restore six different types of halftone image well. Furthermore, experimental results show that our method outperforms the state-of-the-art methods, especially in the restoration of details and textures.

Texture-aware Error Diffusion Algorithm for Multi-level Digital Halftoning

Digital halftoning is a technique for converting a continuous-tone image into a quantized image to reproduce it on a digital printing device. Error diffusion (ED) is an algorithm that has proven to be effective for the halftoning process, and it has been widely applied to digital printing tasks. However, in images reproduced using conventional ED algorithms based on the signal processing theory, the texture of objects is often lost. In this study, we propose a texture-aware ED algorithm for multi-level digital halftoning. First, we generate multiple mapped images with different brightness levels through nonlinear transformation. For each mapped image, we adopt a texture-aware binary error diffusion method to obtain multiple halftone images. Finally, we generate a multi-level halftone image from the multiple halftone images. We test the algorithm on an actual printer, compare the results with those of the current raster image processor software and classical ED algorithms, and observe that our algorithm outputs better results.

Computational experiment of error diffusion dithering for depth reduction in images

The halftone technique is a process that employs patterns formed by black and white dots to reduce the number of gray levels in an image. Due to the tendency of the human visual system to soften the distinction between points with different shades, the patterns of black and white dots produce a visual effect as if the image were composed of shades of gray and dark. This technique is quite old and is widely used in printing images in newspapers and magazines, in which only black (ink) and white (paper) levels are needed. There are several methods for generating halftone images. In this article we explore dithering with error diffusion and an analysis of different halftone techniques is presented using error diffusion to change the depth of the image. The results showed that the depth of the image changes 1/8 per channel, this halftone technique can be used to reduce an image weight, losing information but achieving good results, depending on the context. ontext.

Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review

Inverse halftoning is an ill-posed problem that refers to the problem of restoring continuous-tone images from their halftone versions. Although much progress has been achieved over the last decades, the restored images still suffer from detail loss and visual artifacts. Recent studies show that inverse halftoning methods based on deep learning are superior to other traditional methods, and thus this paper aimed to systematically review the inverse halftone methods based on deep learning, so as to provide a reference for the development of inverse halftoning. In this paper, we firstly proposed a classification method for inverse halftoning methods on the basis of the source of halftone images. Then, two types of inverse halftoning methods for digital halftone images and scanned halftone images were investigated in terms of network architecture, loss functions, and training strategies. Furthermore, we studied existing image quality evaluation including subjective and objective evaluation by experiments. The evaluation results demonstrated that methods based on multiple subnetworks and methods based on multi-stage strategies are superior to other methods. In addition, the perceptual loss and the gradient loss are helpful for improving the quality of restored images. Finally, we gave the future research directions by analyzing the shortcomings of existing inverse halftoning methods.