Sparse Representation Based Inverse Halftoning with Boosted Dictionary1

Halftoning image is widely used in printing and scanning equipment, which is of great significance for the preservation and processing of these images. However, because of the different resolution of the display devices, the processing and display of halftone image are confronted with great challenges, such as Moore pattern and image blurring. Therefore, the inverse halftone technique is required to remove the halftoning screen. In this paper, we propose a sparse representation based inverse halftone algorithm via learning the clean dictionary, which is realized by two steps: deconvolution and sparse optimization in the transform domain to remove the noise. The main contributions of this paper include three aspects: first, we analysis the denoising effects for different training sets and the redundancy of dictionary; Then we propose the improved a sparse representation based denoising algorithm through adaptively learning the dictionary, which iteratively remove the noise of the training set and upgrade the quality of the dictionary; Then the error diffusion halftone image inverse halftoning algorithm is proposed. Finally, we verify that the noise level in the error diffusion linear model is fixed, and the noise level is only related to the diffusion operator. Experimental results show that the proposed algorithm has better PSNR and visual performance than state-of-the-art methods.

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.

Inverse Halftoning Based on Weighted Nuclear Norm Minimization

The inverse halftoning technology refers to restore a continuous-tone image from a halftone image with only bi-level pixes. However, recovering the continuous images from their halftoned ones is normally ill-posed, which making the inverse halftoning algorithm very challenging. In this paper, we propose an optimization model with two alternate projections (TAP) for image inverse halftoning under the weighted nuclear norm minimization (WNNM) framework. The main contributions are two-folds. First, the WNNM nonlocal regularization term is established, which offers a powerful mechanism of nonlocal self-similarity to ensure a more reliable estimation. Second, the alternate minimization projections are formulated for solving the image inverse halftoning, which reconstructs the continuous-tone image without destroying the image details and structures. The experiment results shown that the proposed method outperformed the state of the arts in terms of both objective measurements and subjective visual performance.

ESTIMATING STABILITY OF STRUCTURAL AND BRIGHTNESS PROPERTIES IN IMAGE DIGITAL PROCESSING

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.

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.