Halftone Images Research Articles

A multistage and multiresolution deep convolutional neural network for inverse halftoning

Inverse halftoning is a technology to restore a continuous-tone image from its halftone version. Recent works based on deep convolutional neural network (DCNN) have shown remarkable progresses in this area. However, it is still a hard work to accurately recover the content information, detail information and global information. To this end, we propose a multistage and multiresolution DCNN method for inverse halftoning. The network includes three sub-networks corresponding three stages, each of them is used to restore different information in a progressive manner. Firstly, a tri-resolution analysis network (TRA) is proposed to remove halftone noise dots and then the initial reconstructed image is obtained. Secondly, the detail information is enriched by the detail enhancement sub-network through concatenating the initial reconstructed image and the input halftone image. Finally, a global enhancement sub-network is introduced to adjust information of the whole image. The evaluation results on three public datasets show that the proposed method is superior to the state-of-the-art methods in both visual quality and numerical evaluation. Moreover, the average runtime of the proposed network is 0.14 s for an image with the size of 256×256 pixels, which means the proposed network can meet the requirements of practical applications.

Parallel region growing of half-tone images based on selected average brightness of the area along the growth route

The problem of parallel segmentation of halftone images by brightness for implementation on the basis of programmable logic integrated circuits is considered. Segmentation divides an image into regions formed from pixels with approximately the same brightness, and is a computationally complex operation due to multiple checks of the value of each pixel for the possibility of joining an adjacent region. To speed up segmentation, parallel algorithms for growing areas have been developed, in which processing begins from the neighborhoods of preselected initial growth pixels. The condition of joining an adjacent pixel to an area takes into account the average brightness of the area to limit the variance of its pixel values. Therefore, when each new pixel is added to the area, its average brightness is recalculated. This leads to high time complexity. In some parallel algorithms, the sample mean is calculated in a small window, which makes it possible to slightly reduce the time complexity when matching the window size with the segment sizes. To significantly reduce the temporal complexity, the article proposes a model for the parallel growth of image regions based on a simplified condition for joining adjacent pixels to a region, taking into account the sample average value of the region's brightness along the growth route connecting the boundary pixel of the region and the initial growth pixel through a sequence of pixels used to attach the considered boundary pixel to area. A significant decrease in the temporal complexity of the proposed model of parallel growing of image regions in comparison with the known models is achieved due to a slight increase in the spatial complexity.

Физиологические аспекты восприятия визуальной информации глазодвигательным аппаратом

The individual characteristics of the human visual apparatus are associated with the anatomical and psychophysiological parameters of his body. Based on the EyeTracking technology, the physiological aspects of the perception of visual information by the oculomotor apparatus, which are not associated with active cognitive activity, have been investigated. The individual features in the size of fixation when reading text and examining halftone graphic objects in various people have been investigated. The time durations of fixations in different people, associated with the process of accommodation, as well as the internal structure of fixations, were investigated. It is shown that the trajectory of the gaze shift in fixation has an internal heterogeneous structure. The total trajectory of eye movement in the fixation area is determined by a set of successive clusters. This fixation structure is apparently associated with the processes of restoration of the photosensitivity of rhodopsin in the photoreceptors of the retina. All the above studies of the fixations of various subjects on the basis of various images showed that the oculomotor system, taking into account the physiological characteristics of the visual apparatus, is equally controlled by the "video processor" of the brain when the eye is accommodated to the image elements. And the only objective individual feature of human vision, which uniquely characterizes the perception of graphic information, is the value of the average displacement in fixation. It is she who is the "visiting card" of the subject and remains practically unchanged both when reading and when examining halftone images and in test validation with forced fixation of the gaze.

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 optimization of color halftone visual cryptography scheme based on Bat algorithm

Abstract Visual cryptography is a cryptographic technique that allows visual information to be encrypted so that the human optical system can perform the decryption without any cryptographic computation. The halftone visual cryptography scheme (HVCS) is a type of visual cryptography (VC) that encodes the secret image into halftone images to produce secure and meaningful shares. However, the HVC scheme has many unsolved problems, such as pixel expansion, low contrast, cross-interference problem, and difficulty in managing share images. This article aims to enhance the visual quality and avoid the problems of cross-interference and pixel expansion of the share images. It introduces a novel optimization of color halftone visual cryptography (OCHVC) scheme by using two proposed techniques: hash codebook and construction techniques. The new techniques distribute the information pixels of a secret image into a halftone cover image randomly based on a bat optimization algorithm. The results show that these techniques have enhanced security levels and make the proposed OCHVC scheme more robust against different attacks. The OCHVC scheme achieves mean squared error (MSE) of 95.0%, peak signal-to-noise ratio (PSNR) of 28.3%, normalized cross correlation (NCC) of 99.4%, and universal quality index (UQI) of 99.3% on average for the six shares. Subsequently, the experiment results based on image quality metrics show improvement in size, visual quality, and security for retrieved secret images and meaningful share images of the OCHVC scheme. Comparing the proposed OCHVC with some related works shows that the OCHVC scheme is more effective and secure.

Division of images into areas of local extrema with a monotonic change in pixel brightness

In this paper, the problem of segmentation of halftone images is considered, in which areas of local maxima and minima (extrema) are distinguished with a monotonic change in the brightness of pixels from local extrema to the boundaries of areas. To solve this problem, a mathematical model is proposed and a segmentation algorithm is developed on the basis of counter-wave growing of local extremum regions. The developed algorithm differs from the known segmentation algorithms by using a set of brightness thresholds (by the number of regions), varying by one in each cycle, starting from the values of local extrema, taking into account the increase or decrease in brightness to select adjacent pixels that are attached to the regions formed from these local extrema. The algorithm provides a greater deviation of pixel brightness from the average value within the region compared to known segmentation algorithms. This does not allow evaluating its efficiency using known indicators based on the variance of the brightness within the region. In this regard, estimates of the monotonicity of changes in the brightness of regions are proposed based on a) the shortest distances from each pixel of the region to the corresponding local extremum along the routes determined by the maximum increase (for the region of the local maximum) or decrease (for the region of the local minimum) the brightness of pixels and b) taking into account the number pixels that break the monotony of the segment brightness change. Using these estimates, it is shown that the proposed algorithm provides segmentation of artificial and natural grayscale images with a monotonic change in the brightness of pixels in the areas of local extrema. These properties allow us to consider the developed algorithm as a basis for the selection of texels, spots, low-contrast objects in images.

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.

Secure halftone image steganography based on density preserving and distortion fusion

Most state-of-the-art halftone image steganographic methods measure the flipping distortion according to the statistics of the image database but neglect the characteristic of a single image. In this paper, a halftone image steganography based on density preserving and distortion fusion is proposed for minimizing the flipping distortion. Different from ordinary binary images, halftone images express the texture information by the intensity of local regions that are represented by the pixel density. Based on the density distribution of a halftone image, the embedding costs defined by a single existing distortion function are modified adaptively to maintain the original texture structure as much as possible. Furthermore, to select more appropriate flipped pixels, a distortion fusion strategy is developed from the perspective of anti-steganographic analysis, which can be applied to a batch of distortion functions. Finally, the syndrome-trellis code (STC) is applied to minimize the embedding distortion for playing the advantage of the distortion measurement. Experimental results show that the proposed steganographic scheme achieves great visual quality and strong statistical security without degrading the embedding capacity.

ОЦЕНКА УСТОЙЧИВОСТИ СТРУКТУРНО-ЯРКОСТНЫХ СВОЙСТВ ПРИ ЦИФРОВОЙ ОБРАБОТКЕ ИЗОБРАЖЕНИЙ

The article considers determining the stability assessment of the structural and brightness properties of raster images. The stability assessment refers to the ability of preserving the image properties due to the original image processing by filtering. Halftone versions of the full-color images presented in the RGB color space are accepted as original images. The grayscale images are filtered using the Gaussian, Wiener, and median filtration algorithms. The next step is to use one of the variants of the SSIM algorithm to obtain the structural similarity index between the control halftone image and the filtered image. In order to create a relative and dimensionless metric there has been calculated the area of the polygon S set by the values of the similarity indices relative to the control grayscale image, and the area of the rectangle Sp that contains the area of the polygon S. Images from the international image database TID2008 (image database 2008), TID2013 (image database 2013) were taken as test bitmaps. The official coats of arms of the Russian Federation entities – territories, regions, republics, etc. - were also considered. The resulting stability score is determined either as the geometric mean of the four calculated values, or as their arithmetic mean. The results of testing the groups of bitmap images that are united by a common theme or purpose are presented. From each group of images, the maximum and minimum values of the estimates of the image stability to structural and brightness changes are determined in accordance with the developed heuristic algorithm. The results obtained can be used for comparative evaluation of competing images in order to select the most resistant to structural and brightness changes.

CENTRAL AND LOGARITHMIC CENTRAL IMAGE CHORD TRANSFORMATIONS FOR INVARIANT OBJECT RECOGNITION

Pattern descriptors invariant to rotation, scaling, and translation represents an important direction in the elaboration of the real time object recognition systems. In this article, the new kinds of object descriptors based on chord transformation are presented. There are described new methods of image presentation - Central and Logarithmic Central Image Chord Transformations (CICT and LCICT). It is shown that the CICToperation makes it possible to achieve invariance to object rotation. In the case of implementation of the LCICT transformation, invariance to changes in the rotation and scale of the object is achieved. The possibilities of implementing the CICTand LCICToperations are discussed. The algorithms of these operations for contour images are presented. The possibilities of integrated implementation of CICT and LCICT operations are considered. A generalized CICT operation for a full (halftone) image is defined. The structures of the coherent optical processors that implement operations of basic and integral image chord transformations are presented.

Аdaptive combined image coding with prediction of arithmetic code volume

The problem of increasing the efficiency of coding of halftone images in the space of bit planes of differences in pixel values obtained using differential coding (DPCM – Differential pulse-code modulation) is considered. For a compact representation of DPCM pixel values, it is proposed to use a combined compression encoder that implements arithmetic coding and run-length coding. An arithmetic encoder provides high compression ratios, but has high computational complexity and significant encoding overhead. This makes it effective primarily for compressing the mean-value bit-planes of DPCM pixel values. Run-length coding is extremely simple and outperforms arithmetic coding in compressing long sequences of repetitive symbols that often occur in the upper bit planes of DPCM pixel values. For DPCM bit planes of pixel values of any image, a combination of simple run length coders and complex arithmetic coders can be selected that provides the maximum compression ratio for each bit plane and all planes in general with the least computational complexity. As a result, each image has its own effective combined encoder structure, which depends on the distribution of bits in the bit planes of the DPCM pixel values. To adapt the structure of the combined encoder to the distribution of bits in the bit planes of DPCM pixel values, the article proposes to use prediction of the volume of arithmetic code based on entropy and comparison of the obtained predicted value with the volume of run length code. The entropy is calculated based on the values of the number of repetitions of ones and zero symbols, which are obtained as intermediate results of the run length encoding. This does not require additional computational costs. It was found that in comparison with the adaptation of the combined encoder structure using direct determination of the arithmetic code volume of each bit plane of DPCM pixel values, the proposed encoder structure provides a significant reduction in computational complexity while maintaining high image compression ratios.

РЕАЛІЗАЦІЯ ФІЛЬТРА З ПОСТФІЛЬТРАЦІЙНИМ ПРИЙНЯТТЯМ РІШЕННЯ НА МІКРОПРОЦЕСОРНИХ АРХІТЕКТУРАХ З ВЕКТОРНИМ РОЗШИРЕННЯМ ДЛЯ ЗАБЕЗПЕЧЕННЯ ПОКАЗНИКІВ ЕФЕКТИВНОСТІ СУДОВОЇ ЕКСПЕРТИЗИ

Наводяться результати досліджень засобів забезпечення показників ефективності судової експертизи зображень, у тому числі й цифрових. Проводиться аналіз основних завдань, що стоять при експертизі зображень, і методи їх розв’язання. Основною проблемою таких досліджень є недостатня автоматизація процесу обробки зображень при експертизі. Об’єктом дослідження є процес фільтрації зображень, предметом досліджень – фільтри, які використовуються при обробці зображень. В результаті аналізу реалізацій детекторів шуму встановлено, що вони є обчислювально складними. А апаратні витрати на реалізацію алгоритму на сучасних мікропроцесорах і програмованих інтегральних схемах можуть істотно обмежувати застосування таких алгоритмів у додатках, що вимагають обробки в реальному часі. Метою статті є побудова швидкодіючої реалізації фільтра з постфільтраційним прийняттям рішення на сучасних процесорних архітектурах. Наведено результати аналізу можливості використання векторних інструкцій сучасних процесорних архітектур, розглянуто алгоритми векторизації сортування для ефективної реалізації підпрограми пошуку медіанного значення всередині поточного і проведено моделювання фільтра з постфільтраційним прийняттям рішення з метою з’ясування придатності для використання в задачах реального часу. В результаті проведених досліджень вперше запропоновано метод векторизованої реалізації фільтра з постфільтраційним прийняттям рішення, придатний для процесорів з набором команд SIMD ARM NEON, Intel SSE або AVX; розглянуто використання сортувальних мереж як алгоритму пошуку медіани для процесорів з векторним розширенням; вперше побудовано реалізацію фільтра описаним методом для процесора ARM Cortex-A9 в складі SOC Intel Cyclone® V SE 5CSEBA6U23I7NDK; проведено моделювання роботи фільтра на ARM Cortex-A9. Швидкість обробки зображення 512x512 пікселів становила понад 500 кадрів на секунду. Швидкість обробки напівтонових зображень FullHD – понад 60 кадрів на секунду.

XOR-based visual secret sharing scheme using pixel vectorization

The XOR operation has improved the recovery effect of the visual secret sharing schemes. Various visual secret sharing schemes based on XOR operation are available in the literature with some limitations, e.g. direct non-applicability of the scheme to the gray-scale images and reduced quality of the reconstructed secret image. Many existing visual secret sharing schemes in the literature, firstly converts grayscale secret image into a halftone image then creates the shares. This paper proposes XOR-based (n,n) − V CSXOR visual secret sharing scheme using pixel vectorization. It diffuses and disguise secret information into multiple meaningless shares prior to allocating participants of the group having no clue about the secret information. The scheme also resolves various problems like pixel expansion, contrast-loss, explicit codebook requirement, limitation on number of participants and lossy recovery of the secret image. The proposed scheme makes use of implicit codebook and pixel vectorization to encode gray secret image directly without converting to halftone image, into multiple random looking shares. Also applicable to binary image. Reveal a secret image perfectly by XOR-ing all share. The efficacy of the proposed scheme is verified by numerical illustrations, experimental results and comparative analysis. We found that proposed scheme outperform in comparison to the state-of-the-art approaches.

Measuring the length of objects on scanning probe microscope images using curvature detectors

The article is devoted to the automatic measurement of objects longitudinal dimensions on images obtained by probe microscopy. The solution of this problem can be relevant for quality control of microelectronics, nanotechnics products and materials. Existing tools for objects length measuring are compared by means of test image containing geometric figures with known dimensions. The advantages of software surface curvature detectors, intended for objects lengths measuring directly on a halftone image by forming the skeleton of an object with a surface curvature detector, are shown. A two-dimensional “Circle” detector, based on the curvature analysis of raster images line and column profilograms, was used for the measuring. The curvature was estimated based on the area of the figure bounded by the profilogram at a predefined interval. Features of measuring the length of objects using curvature maxima are considered. It is shown that the curvature detector allows to more accurately determine the lengths of objects with overlapping contours and a significant brightness range. Algorithms of the detector operation, formation of the object skeleton and determination of its length are described. The results of investigation confirming the performance of the presented algorithms are presented. Comparative analysis with existing length measurement tools, performed on magnetic disk domains and nanopolymer fibers images, showed a more correct detector operation in sceletonization of object and measuring its length.

Wavelet transform of a halftone image in a finite field

Wavelet transform of a halftone image in a finite field

A Comprehensive Halftone Image Quality Evaluation of First- and Second-order FM Halftones

Halftoning is a crucial part of image reproduction in print. For large format prints, especially at higher resolutions, it is important to have very fast and computationally feasible halftoning methods of good quality. The authors have already introduced an approach to obtain image-independent threshold matrices generating both first- and second-order frequency modulated (FM) halftones with different clustered dot sizes. Predetermined and image-independent threshold matrices make the proposed halftoning method a point-by-point process and thereby very fast. In this article, they report a comprehensive quality evaluation of first-and second-order FM halftones generated by this technique and compare them with each other, employing several quality metrics. These generated halftones are also compared with error diffusion (ED) halftones employing two different error filters. The results indicate that the second-order FM halftoning with small clustered dot size performs best in almost all studied quality aspects than the first-and second-order FM halftoning with larger clustered dot size. It is also shown that the first- and second-order FM halftones with small clustered dot sizes are of almost the same quality as ED halftones using Floyd–Steinberg error filter and of higher quality than halftones generated by ED employing Jarvis, Judice, and Ninke error filter.

Structure-Aware Halftoning Using the Iterative Method Controlling the Dot Placement

Many image reproduction devices, such as printers, are limited to only a few numbers of printing inks. Halftoning, which is the process to convert a continuous-tone image into a binary one, is, therefore, an essential part of printing. An iterative halftoning method, called Iterative Halftoning Method Controlling the Dot Placement (IMCDP), which has already been studied by research scholars, generally results in halftones of good quality. In this paper, we propose a structure-based alternative to this algorithm that improves the halftone image quality in terms of sharpness, structural similarity, and tone preservation. By employing appropriate symmetrical and non-symmetrical Gaussian filters inside the proposed halftoning method, it is possible to adaptively change the degree of sharpening in different parts of the continuous-tone image. This is done by identifying a dominant line in the neighborhood of each pixel in the original image, utilizing the Hough Transform, and aligning the dots along the dominant line. The objective and subjective quality assessments verify that the proposed structure-based method not only results in sharper halftones, giving more three-dimensional impression, but also improves the structural similarity and tone preservation. The adaptive nature of the proposed halftoning method makes it an appropriate algorithm to be further developed to a 3D halftoning method, which could be adapted to different parts of a 3D object by exploiting both the structure of the images being mapped and the 3D geometrical structure of the underlying printed surface.

Two-layered ant colony system to improve engraving robot’s efficiency based on a large-scale TSP model

Laser engraving is an essential tool of automatic drawings and 3D printers. When the laser engraving tasks become large and complicated, engraving process will be time-consuming. To improve the time and energy efficiency, the trajectory optimization of laser engraving is studied. The trajectory of laser engraving robot is modelled as a large-scale traveling salesman problem (TSP), by converting grayscale images into halftone images. To solve the large-scale TSP, two-layered ant colony system (ACS) is newly proposed to combine k-means, top-layer ACS, and bottom-layer ACS. Finally, we use the presented algorithm to optimize the path of four engraving instances which include tens of thousands of discrete points. Experimental results show that this method can reduce laser engraving time by about 50% compared with traditional engraving methods.

RETRACTED ARTICLE: An effective secret image sharing using quantum logic and GPGPU based EDNN super-resolution

This paper presented an effective secret image sharing with super-resolution utilizing quantum logic and enthalpy based adaptive deep neural network. The proposed technique is processed as; at the sender side, initially secret input image is converted into a halftone image format by utilizing Error diffusion with varying thresholds (EDVT) method. Then in share generation phase, shares are produced with the basis matrix. Here, the basis matrix is created utilizing the quantum logic methodology. Then in embedding phase, discrete wavelet transform (DWT) is utilized for encoding shares. At the receiver side, encoded image is reconstructed using XOR operation and results the low-resolution image. Finally, enthalpy based adaptive deep neural network (EDNN) is designed with the General Purpose Graphic Processing Unit (GPGPU) to enhance the resolution of the reconstructed images and to lessen the time complexity of deep learning. Here, the EDNN is adapted with the enthalpy based normalization to mitigate the over fitting in layers of deep neural network. Furthermore, the proficiency of the proposed work improved in terms of normalized cross correlation, normalized absolute error, peak signal to noise ratio, mean square error and execution time by deploying images among CPU and GPGPU in an enhanced manner.