Halftone Images Research Articles

Boolean-based Two-in-One Secret Image Sharing by Adaptive Pixel Grouping

The two-in-one secret image sharing (TiOSIS) technique is a hybrid scheme that protects a secret image by combining visual cryptography (VCS) and polynomial-based secret image sharing (PSIS). There are two decoding methods available in TiOSIS: stacking-to-see decryption and lossless image recovery. However, the majority of current TiOSIS methods use Lagrange interpolation to precisely reconstruct the secret, which would result in intense computations. In this article, an efficient TiOSIS scheme using Boolean XOR operation for lossless image recovery is proposed. The proposed scheme consists of three building blocks: shared data generation, shadow construction, and image decryption. In shared data generation, the grayscale secret image is processed by a Boolean-based SIS to derive the shared bits. In shadow construction, an adaptive pixel grouping (APG) strategy is utilized to determine a grouping pattern. The halftone image adjustment algorithm is adopted to generate a suitable halftone image. With the grouping pattern and halftone image, we construct the shadows via the group-pixel embedding and sharing approach. In image decryption, we can reveal the secret image by stacking-to-see decoding or Boolean-based lossless image recovery. Extensive experiments and comparisons are illustrated to show the effectiveness and benefits of the proposed scheme.

Experimental Curves Segmentation Using Variable Resolution

A new segmentation method of signals distorted by noise is proposed. Unlike other known methods, for example, the Canny method, a priori data on interference and / or a signal (image) is not used. Segmentation of signals and halftone images distorted by interference is one of the oldest problems in computer vision. But human vision solves this task almost independently of our consciousness. It was discovered for visual neurons, that sizes of receptive fields' excitatory zones change during visual act, which eventually mean dynamical changes in visual system's resolution i.e. coarse-tofine phenomenon in living organism. We assumed that „coarse-to-fine“ phenomenon, i.e. several different resolutions, is used in human vision to segment images. A „coarse-to-fine“ algorithm for segmentation of experimental graphs was developed. The main difference of algorithm mentioned above from others is that decision is made taking into account all partial solutions for all resolutions being used. This ensures stability of final global solution. The algorithm verification results are presented. It is expected that the method can naturally be expanded to segmentation of halftone images.

Modeling steganocontent extraction attempts with different lengths stack sampling series of images blocks

The results obtained by using different lengths of sample stacks of runs in simulating the attempts of unauthorized extraction (attack) of steganocontent "protected" by implementing the mechanism of inter-block multiplexing of the parameters of the run lengths of image blocks have been considered in the article. The relationship between the parameters of processing the content (namely, halftone images) and the number of series, as well as the combinatorics of the component elements of the obtained pairs of series parameters, which are the objects of inter-block multiplexing has been demonstrated. It is concluded that the simultaneous use of 2-level data multiplexing significantly extends the capabilities to withstand content attack attempts. It has been found that the use of blocks of higher dimensionality, significantly reduces the role current parameters of the series reliance (base) blocks, in breaking the structure of the original images (those. original content). It is noted that use of two levels of multiplexing of output data at once significantly increases the resistance of the content to attempts at its unauthorized extraction, leading to large distortions in the attacked image, in case of incorrect selection of the active processing parameters.

Objects outline delineation by nonliniear filtration methods for boundary pixels

A processing of border pixel from pixel map originate problem while work with outline objects segmentation algorithm on bitmap images, which are based on 2D convolution operation. The problem correspond with addition set of pixels which are outside pixel matrix border of processing image. This problem is actual also in other algorithms, which have a common feature – 2D convolution operation. The level of importance are defined by multi used nonlinear filters to border pixels and then union a few images without distortions at the boundaries. The existing methods of pixel set redefining on pre processing step are primitive and affects on subsequent stages of processing. This affects exist between methods of redefining border pixels and algorithms of outline objects segmentation: Sharra, Pruitta, Sobel. The object of investigation is pixel map of halftone image. The subject of investigation is relation assessment between algorithms of outline objects segmentation with methods redefining border pixels. The main target is describing of features special methods to redefining border pixels which positively affecting the subsequent evaluation of the responses for this pixels by algorithms of outline objects segmentation. Analysis, the influence of the algorithm for the outline objects segmentation on the boundary pixels from the preliminary selection of methods of redefinition is carried out by example of combining several satellite images along the outline of urban infrastructure. For simplicity and by necessary of large data volume visualization (pixel half tone matrix size) presented graphs: halftone codes for redefining and border pixels, pixels requests for the algorithm of outline objects segmentation. By analysis criteria and characteristics of an effective method of redefining boundary pixels are described with correspond affects on algorithms of outline objects segmentation on bitmap images.

Development of a Technique for the Spectral Description of Curves of Complex Shape for Problems of Object Classification

Vascular pathology symptoms can be determined by retinal image segmentation and classification. However, the retinal images from non-invasive diagnostics have a complex structure containing tree-like vascular beds, multiple segment boundaries, false segments, and various distortions. It should be noted that complex structure images’ segmentation does not always provide a single solution. Thus, the goal is to increase the efficiency of vascular diagnostics. This study aims to develop a technique for describing the geometric properties of complexly structured image segments used for classifying vascular pathologies based on retinal images. The advantages and disadvantages of the existing methods and algorithms of segmentation were considered. The most effective use areas of the mentioned methods and algorithms are revealed. Through detecting retinal thrombosis, the algorithm's efficiency for constructing a mathematical model of an arbitrary shape segment based on the morphological processing of binary and halftone images was justified. A modified variant of this algorithm based on the spectral analysis procedure of arbitrary shape boundary curves was used for the spectral description of complex shape curves for classifying vascular pathologies based on retinal images. Two approaches have been developed. The first one allows obtaining a closing segment of the curve from a symmetric mapping of the initial parametric curves. The second involves intelligent data processing and obtaining contours of minimum thickness, forming convex sets. The results of experiments confirm the possibility of practical use of the developed technique to solve problems of vascular pathology classification based on retinal images, showing the correct forecast probability was 0.93 with all associated risk factors. Doi: 10.28991/ESJ-2022-06-06-015 Full Text: PDF

Deep residual neural networks for inverse halftoning

This paper presents a simple technique to perform inverse halftoning using the deep learning framework. The proposed method inherits the usability and superiority of deep residual learning to reconstruct the halftone image into the continuous-tone representation. It involves a series of convolution operations and activation function in forms of residual block elements. We investigate the usage of pre-activation function and standard activation function in each residual block. The experimental section validates the proposed method ability to effectively reconstruct the halftone image. This section also exhibits the proposed method superiority in the inverse halftoning task compared to that of the handcrafted feature schemes and former deep learning approaches. The proposed method achieves 30.37 dB and 0.9481 on the average peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) scores, respectively. It gives the improvements around 1.67 dB and 0.0481 for those values compared to the most competing scheme.

An inverse halftoning method for various types of halftone images based on multi-scale generative adversarial network

The inverse halftoning method based on deep neural network has been widely studied in last few years. However, the existing methods only apply single-size convolution kernel to extract image features and train the proposed model with only one-scale loss function, which leads to blurring details and residual halftone noise patterns in the restored continuous-tone image. To alleviate these problems, this paper proposes an inverse halftone method for various types of halftone images based on multi-scale generative adversarial network. In the generative model, an elaborated multi-scale feature extraction model is firstly designed to extract halftone image features in parallel, so as to obtain more comprehensive image information from the input halftone image and promote the effective fusion of the extracted features. Moreover, a detail enhancement subnetwork is employed to fine-tune the image details. In adversarial model, a multi-scale discriminator is used to further enhance the image details by learning the distribution characteristics of image information. The experiments on different types of halftone images show that the proposed method significantly outperforms the existing state-of-art methods.

Estimation of Motion Sickness in Automated Vehicles using Stereoscopic Visual Simulation

Structure-aware halftoning algorithms aim at improving their non-structure-aware version by preserving high-frequency details, structures, and tones and by employing additional information from the input image content. The recently proposed achromatic structure-aware Iterative Method Controlling the Dot Placement (IMCDP) halftoning algorithm uses the angle of the dominant line in each pixel’s neighborhood as supplementary information to align halftone structures with the dominant orientation in each region and results in sharper halftones, gives a more three-dimensional impression, and improves the structural similarity and tone preservation. However, this method is developed only for monochrome halftoning, the degree of sharpness enhancement is constant for the entire image, and the algorithm is prohibitively expensive for large images. In this paper, we present a faster and more flexible approach for representing the image structure using a Gabor-based orientation extraction technique which improves the computational performance of the structure-aware IMCDP by an order of magnitude while improving the visual qualities. In addition, we extended the method to color halftoning and studied the impact of orientation information in different color channels on improving sharpness enhancement, preserving structural similarity, and decreasing color reproduction error. Furthermore, we propose a dynamic sharpness enhancement approach, which adaptively varies the local sharpness of the halftone image based on different textures across the image. Our contributions in the present work enable the algorithm to adaptively work on large images with multiple regions and different textures.

Information-Theoretic Approach to the Problem of Automatic Image Recognition

The problem of automatic half-tone image recognition from the large database is formulated. Its decision using data on a problem of statistical classification of discrete objects on the basis of the minimum information discrimination principle is proposed. The new probabilistic model of a half-tone image is studied. On the basis of this model, a new image recognition algorithm using the minimum Kullback–Leibler information discrimination criterion is synthesized. It is proved that this criterion is optimal in Bayesian terms for the classification problem of discrete random objects, including digital images. The program and results of experimental research on the face recognition problem are presented. It is shown that the accuracy of image recognition with various light conditions on the basis of the offered criterion is essentially higher than the accuracy of traditional methods.

A novel steganography method for binary and color halftone images.

Digital steganography is the science of establishing hidden communication on electronics; the aim is to transmit a secret message to a particular recipient using unsuspicious carriers such as digital images, documents, and audio files with the help of specific hiding methods. This article proposes a novel steganography method that can hide plaintext payloads on digital halftone images. The proposed method distributes the secret message over multiple output copies and scatters parts of the message randomly within each output copy for increased security. A payload extraction algorithm, where plain carrier is not required, is implemented and presented as well. Results gained from conducted objective and subjective tests prove that the proposed steganography method is secure and can hide large payloads.

Fabricable Multi‐Scale Wang Tiles

AbstractWang tiles, also known as Wang dominoes, are a jigsaw puzzle system with matching edges. Due to their compactness and expressiveness in representing variations, they have become a popular tool in the procedural synthesis of textures, height fields, 3D printing and representing other large and non‐repetitive data. Multi‐scale tiles created from low‐level tiles allow for a higher tiling efficiency, although they face the problem of combinatorial explosion. In this paper, we propose a generation method for multi‐scale Wang tiles that aims at minimizing the amount of needed tiles while still resembling a tiling appearance similar to low‐level tiles. Based on a set of representative multi‐scale Wang tiles, we use a dynamic generation algorithm for this purpose. Our method can be used for rapid texture synthesis and image halftoning. Respecting physical constraints, our tiles are connected, lightweight, independent of the fabrication scale, able to tile larger areas with image contents and contribute to “mass customization”.

Balanced pixel interpolation algorithm when identifying the object’s contours on the borders of the image

The operation of algorithms for identifying object’s contours using two-dimensional convolution operation on the boundaries of raster images depends entirely on the location of the transformed pixel in the pixel matrix. The problem is related to the need for the convolution algorithm itself to use an additional set of pixels located outside the pixel matrix of the processed image [1, 2, 3]. In this paper, an algorithm for balanced interpolation of pixels at image boundaries in addition to algorithms based on statistical averages [4, 5] is proposed. Various sets of coefficients which are located at a certain pixel layout and participate in the interpolation algorithm is considered. A comparative analysis of the results of the balanced interpolation algorithm with the results using the arithmetic mean methods is carried out. Also, an example of using a new algorithm to solve the problem of the object’s contours identification on the borders of a raster halftone image is presented. The described algorithm is easy to understand, use and integrate with other algorithms and it does not require significant computing power.

Making Pictorial Print: Media Literacy and Mass Culture in British Magazines 1885–1918

Making Pictorial Print: Media Literacy and Mass Culture in British Magazines 1885–1918

Application of mathematical methods and machine learning algorithms for classification of X-ray images

The relevance of the topic, in particular, if to take one of the information flows, whether it is the action of a human factor or a specific object, then it is true that special processing of the machine learning language and automatic information output significantly optimize human life. With the help of neural networks and their chest radiography is one of the most accessible radiological studies for screening and diagnosis of many lung diseases a special machine learning language is to study the flow of information about it and the same object in real time using neural networks. The article describes the terminology of the problem of X-ray recognition using machine learning methods and algorithms, examines the relevance of the problem, and analyzes the current state of the problem in the field of X-ray recognition. The aspects of the problem being solved, identified during the analysis, in the form of solved problems, approaches, methods, information technologies used, tools and software solutions to the problem are noted The paper is devoted to the description of a modified method of fuzzy clustering of halftone images, which at each iteration performs a dynamic transformation of the source data based on a singular decomposition with automatic selection of the most significant columns of the matrix of left singular vectors. The results of experimental studies were obtained by processing X-ray images. As a result of testing a neural network model, in the output layer of which a sigmoidal activation function was used to activate neurons, and an algorithm was used as an optimization method, the best values of accuracy and completeness were obtained: accuracy – 94.2 During testing, the neural network showed an accuracy of pneumonia recognition equal to 94,27 %

Novel Visual Secret Sharing Scheme for the QR Code with Meaningful Shares

Visual secret sharing (VSS) scheme can share the secret image with several shares. Shares are performed by the decoding operation to restore the secret image. VSS scheme is mainly used for the secure transmission of images. Quick response (QR) code is often used by people on the Internet. It can be encrypted by the VSS scheme to enhance the security of its transmission. The share is meaningless that will arouse the suspicion of the attacker. The security of the VSS scheme is decreased. This paper proposes a novel VSS scheme for the QR code with meaningful images to solve this problem. Two continue tone images are converted to halftone images. The halftone image is modified to generate the share. Every share is meaningful, not meaningless. Two shares are performed the decoding operation to restore the secret QR code. The restored QR code can be correctly decoded by the general decoder. Experimental results prove that the proposed scheme is feasible.

Glossiness Index of Objects in Halftone Color Images Based on Structure and Appearance Distortion.

This paper proposes an objective glossiness index for objects in halftone color images. In the proposed index, we consider the characteristics of the human visual system (HVS) and associate the image’s structure distortion and statistical information. According to the difference in the number of strategies adopted by the HVS in judging the difference between images, it is divided into single and multi-strategy modeling. In this study, we advocate multiple strategies to determine glossy or non-glossy quality. We assumed that HVS used different visual mechanisms to evaluate glossy and non-glossy objects. For non-glossy images, the image structure dominated, so the HVS tried to use structural information to judge distortion (a strategy based on structural distortion detection). For glossy images, the glossy appearance dominated; thus, the HVS tried to search for the glossiness difference (an appearance-based strategy). Herein, we present an index for glossiness assessment that attempts to explicitly model the structural dissimilarity and appearance distortion. We used the contrast sensitivity function to account for the mechanism of halftone images when viewed by the human eye. We estimated the structure distortion for the first strategy by using local luminance and contrast masking; meanwhile, local statistics changing in the spatial frequency components for skewness and standard deviation were used to estimate the appearance distortion for the second strategy. Experimental results showed that these two mixed-distortion measurement strategies performed well in consistency with the subjective ratings of glossiness in halftone color images.

Modeling the 3D shape of a fingernail and pre-distorting an image to be printed on the fingernail to yield the correct appearance

In this paper, we explore a technique for printing on the curved surface of a human fingernail. We start by reproducing the shape of the fingernail via a 3D model. In our future work, we will apply a distortion to the halftone image to correct the deformation of the printed image.

A Machine Learning Approach to Design of Aperiodic, Clustered-Dot Halftone Screens via Direct Binary Search.

Aperiodic, clustered-dot, halftone patterns have recently become popular for commercial printing of continuous-tone images with laser, electrophotographic presses, because of their inherent stability and resistance to moiré artifacts. Halftone screens designed using the multistage, multipass, clustered direct binary search (MS-MP-CLU-DBS) algorithm can yield halftone patterns with very high visual quality. But the characteristics of these halftone patterns depend on three input parameters for which there are no known formulas to choose their values to yield halftone patterns of a certain quality level and scale. Using machine learning methods, two predictors are developed that take as input these three parameters. One predicts the quality level of the halftone pattern. The other one predicts the scale of the halftone pattern. To provide ground truth information for training these predictors, human subjects viewed a large number of halftone patches generated from MS-MP-CLU-DBS-designed screens and assigned each patch to one of four quality levels. For each patch, the location of the peak in the radially averaged power spectrum (RAPS) is calculated as a measure of the scale or effective line frequency of the pattern. Experimental results demonstrate the accuracy of the two predictors and the effectiveness of screen design procedures based on these predictors to generate both monochrome and color high quality halftone images.

Analysis of Mechanism and Characteristics of Starvation Defect at Edge of Halftone Image in Development Process of Electrophotography

Analysis of Mechanism and Characteristics of Starvation Defect at Edge of Halftone Image in Development Process of Electrophotography

Filtering grayscale images using the Kalman filter

This article aims to explore the possibility of using the Kalman filter to filter images. The relevance of the study lies in the fact that at present the tasks of image processing have become of great importance in many areas, such as industry, science, medicine, space industry and agriculture. Methods for improving image quality are of great applied and scientific interest for the agricultural sector, since machine vision methods are now widely used in assessing the condition of agricultural plants, soil condition, sorting of agricultural products, controlling unmanned agricultural machines, etc. The purpose of this work is to develop an algorithm and software for filtering grayscale images. The article consists of four parts: Introduction, Materials and methods, Results, Conclusions. The first part describes the relevance of the topic, discusses the reasons for obtaining noisy images. The second part describes the Kalman filter algorithm as applied to image filtering problems. In the third part, the results of the software implementation of the developed algorithm are considered, which make it possible to evaluate the quality of image filtering. In the fourth part conclusions are drawn and summed up. The main results of the work are the algorithmic implementation of noise removal from halftone images grayscale images using a software tool developed as part of these studies.