Inverse halftoning via MAP estimation

There has been a tremendous amount of research in the area of image halftoning, where the goal has been to find the most visually accurate representation given a limited palette of gray levels (often just two, black and white). This paper focuses on the inverse problem, that of finding efficient techniques for reconstructing high-quality continuous-tone images from their halftoned versions. The proposed algorithms are based on a maximum a posteriori (MAP) estimation criteria using a Markov random field (MRF) model for the prior image distribution. Image estimates obtained with the proposed model accurately reconstruct both the smooth regions of the image and the discontinuities along image edges. Algorithms are developed and example gray-level reconstructions are presented generated from both dithered and error-diffused halftone originals. Application of the technique to the problems of rescreening and the processing of halftone images are shown.     

Halftone visual cryptography.

Visual cryptography encodes a secret binary image (SI) into n shares of random binary patterns. If the shares are xeroxed onto transparencies, the secret image can be visually decoded by superimposing a qualified subset of transparencies, but no secret information can be obtained from the superposition of a forbidden subset. The binary patterns of the n shares, however, have no visual meaning and hinder the objectives of visual cryptography. Extended visual cryptography [1] was proposed recently to construct meaningful binary images as shares using hypergraph colourings, but the visual quality is poor. In this paper, a novel technique named halftone visual cryptography is proposed to achieve visual cryptography via halftoning. Based on the blue-noise dithering principles, the proposed method utilizes the void and cluster algorithm [2] to encode a secret binary image into n halftone shares (images) carrying significant visual information. The simulation shows that the visual quality of the obtained halftone shares are observably better than that attained by any available visual cryptography method known to date.     

A multiscale error diffusion technique for digital halftoning

A new digital halftoning technique based on multiscale error diffusion is examined. We use an image quadtree to represent the difference image between the input gray-level image and the output halftone image. In iterative algorithm is developed that searches the brightest region of a given image via "maximum intensity guidance" for assigning dots and diffuses the quantization error noncausally at each iteration. To measure the quality of halftone images, we adopt a new criterion based on hierarchical intensity distribution. The proposed method provides very good results both visually and in terms of the hierarchical intensity quality measure.     

Model-based digital halftoning

Digital halftoning is the process of generating a pattern of pixels with a limited number of colors that, when seen by the human eye, is perceived as a continuous-tone image. Digital halftoning is used to display continuous-tone images in media in which the direct rendition of the tones is impossible. The most common example of such media is ink or toner on paper, and the most common rendering devices for such media are, of course, printers. Halftoning works because the eye acts as a spatial low-pass filter that blurs the rendered pixel pattern, so that it is perceived as a continuous-tone image. Although all halftoning methods rely at least implicitly, on some understanding of the properties of human vision and the display device, the goal of model-based halftoning techniques is to exploit explicit models of the display device and the human visual system (HVS) to maximize the quality of the displayed images. Based on the type of computation involved, halftoning algorithms can be broadly classified into three categories: point algorithms (screening or dithering), neighborhood algorithms (error diffusion), and iterative algorithms [least squares and direct binary search (DBS)]. All of these algorithms can incorporate HVS and printer models. The best halftone reproductions, however, are obtained by iterative techniques that minimize the (squared) error between the output of the cascade of the printer and visual models in response to the halftone image and the output of the visual model in response to the original continuous-tone image.     

Embedded multilevel error diffusion

We present an algorithm for image browsing systems that embeds the output of binary Floyd-Steinberg (1975) error diffusion, or a low bit-depth gray-scale or color error diffused image into higher bit-depth gray-scale or color error diffused images. The benefits of this algorithm are that a low bit-depth halftoned image can be directly obtained from a higher bit-depth halftone for printing or progressive transmission simply by masking one or more bits off of the higher bit-depth image. The embedding can be done in any bits of the output, although the most significant or the least significant bits are most convenient. Due to constraints on the palette introduced by embedding, the image quality for the higher bit-depth halftone may be reduced. To preserve the image quality, we present algorithms for color palette organization, or binary index assignment, to be used as a preprocessing step to the embedding algorithm.     

A dual interpretation for direct binary search and its implicationsfor tone reproduction and texture quality

The direct binary search (DBS) algorithm employs a search heuristic to minimize the mean-squared perceptually filtered error between the halftone and continuous-tone original images. Based on an efficient method for evaluating the effect on the mean squared error of trial changes to the halftone image, we show that DBS also minimizes in a pointwise sense the absolute error under the same visual model, but at twice the viewing distance associated with the mean-squared error metric. This dual interpretation sheds light on the convergence properties of the algorithm, and clearly explains the tone bias that has long been observed with halftoning algorithms of this type. It also demonstrates how tone bias and texture quality are linked via the scale parameter, the product of printer resolution and viewing distance. Finally, we show how the tone bias can be eliminated by tone-correcting the continuous-tone image prior to halftoning it.     

Robust Watermarking with Kernels-Alternated Error Diffusion and Weighted Lookup Table in Halftone Images

A halftone watermarking method of high quality, robustness, and capacity flexibility is presented in this paper. An objective halftone image quality evaluation method based on the human visual system obtained by a least-mean-square algorithm is also introduced. In the encoder, the kernels-alternated error diffusion (KAEDF) is applied. It is able to maintain the computational complexity at the same level as ordinary error diffusion. Compared with Hel-Or using ordered dithering, the proposed KAEDF yields a better image quality through using error diffusion. We also propose a weighted lookup table (WLUT) in the decoder instead of lookup table (LUT), as proposed by Pei and Guo, so as to achieve a higher decoded rate. As the experimental results demonstrate, this technique is able to guard against degradation due to tampering, cropping, rotation, and print-and-scan processes in error-diffused halftone images.     

An adaptive inverse halftoning algorithm

A class of inverse halftoning algorithms that recovers grayscale (continuous-tone) images from halftone images is proposed. The basic structure is an optimized linear filter. Then, a properly designed adaptive postprocessor is employed to enhance the recovered image quality. Finally, a multistage space-varying algorithm is developed that uses the basic linear filter structure as before but with spatially adaptive parameters.     

Adaptive error diffusion and its application in multiresolutionrendering

Error diffusion is a procedure for generating high quality bilevel images from continuous-tone images so that both the continuous and halftone images appear similar when observed from a distance. It is well known that certain objectionable patterning artifacts can occur in error-diffused images. Here, we consider a method for adjusting the error-diffusion filter concurrently with the error-diffusion process so that an error criterion is minimized. The minimization is performed using the least mean squares (LMS) algorithm in adaptive signal processing. Using both raster and serpentine scanning, we show that such an algorithm produces better halftone image quality compared to traditional error diffusion with a fixed filter. Based on the adaptive error-diffusion algorithm, we propose a method for constructing a halftone image that can be rendered at multiple resolutions. Specifically, the method generates a halftone from a continuous tone image such that if the halftone is down-sampled, a binary image would result that is also a high quality rendition of the continuous-tone image at a reduced resolution. Such a halftone image is suitable for progressive transmission, and for cases where rendition at several resolutions is required. Cases for noninteger scaling factors are also considered.     

High-capacity data hiding in halftone images using minimal-error bit searching and least-mean square filter.

In this paper, a high-capacity data hiding is proposed for embedding a large amount of information into halftone images. The embedded watermark can be distributed into several error-diffused images with the proposed minimal-error bit-searching technique (MEBS). The method can also be generalized to self-decoding mode with dot diffusion or color halftone images. From the experiments, the embedded capacity from 33% up to 50% and good quality results are achieved. Furthermore, the proposed MEBS method is also extended for robust watermarking against the degradation from printing-and-scanning and several kinds of distortions. Finally, a least-mean square-based halftoning is developed to produce an edge-enhanced halftone image, and the technique also cooperates with MEBS for all the applications described above, including high-capacity data hiding with secret sharing or self-decoding mode, as well as robust watermarking. The results prove much sharper than the error diffusion or dot diffusion methods.     

基于NSST图像特征的多聚焦图像融合方法

针对广泛存在于多聚焦图像融合方法中的局部图像细节不清晰的现状,提出一种基于非下采样剪切波变换(NSST)图像特征的多聚焦图像融合方法。利用NSST对待融合源图像进行多尺度、多方向稀疏分解,分别获取低频和一系列高频子带图像。通过空间频率和局部能量确定融合后的低频子带系数,利用边缘检测算子直接获取高频子带图像中的细节和边缘信息,并采取NSST反变换得到最终融合结果图像。仿真实验结果表明,同现有的几种经典算法相比,本文提出的方法获得的结果图像拥有更清晰的视觉效果、更理想的客观指标效果以及更高的算法运行效率。     

Self-conjugate watermarking technique for halftone images

A novel method for hiding visual patterns in a single error diffused halftone image is presented called self-conjugate error diffusion (SCED). The hidden patterns appear on the halftone image when the halftone image is folded or is overlapped with its own rotated version. Simulation results show that the halftone images have good visual quality and the hidden pattern is clearly visible.     

一种改进的半调图像分类算法

半调图像分类在逆半调过程中是非常关键的步骤。文献[1]提出的神经网络分类算法利用增强的一维相关性,可以对半调图像进行适当的分类,但分类精度不够高。在神经网络分类算法的基础上,通过计算图像的灰度共生矩阵,进而提取图像的纹理特征来对图像进行分类。实验表明,改进后的算法可提高分类的精度。     

Watermarking in halftone images with parity-matched error diffusion

In this paper, a halftone watermarking technique of high watermark rate, robustness, and watermark-rate flexibility is presented. This technique, namely the parity-matched error diffusion (PMEDF) method, is capable of achieving an embedded watermark rate as high as 6.25-25% with good image quality and without the need for the original image as the reference for the decoding. The proposed PMEDF method employs the high efficient parity-matching strategy to spread a watermark bit to each pixel of a block of the host image. Thus, the majority voting mechanism can be applied for decoding to achieve an advantage of high robustness. As documented in the experimental results, this technique is capable of guarding against degradation, due to cropping, tampering, as well as print-and-scan process in error-diffused halftone images.     

Hiding color watermarks in halftone images using maximum-similarity binary patterns

This paper presents a halftoning-based watermarking method that enables the embedding of a color image into binary black-and-white images. To maintain the quality of halftone images, the method maps watermarks to halftone channels using homogeneous dot patterns. These patterns use a different binary texture arrangement to embed the watermark. To prevent a degradation of the host image, a maximization problem is solved to reduce the associated noise. The objective function of this maximization problem is the binary similarity measure between the original binary halftone and a set of randomly generated patterns. This optimization problem needs to be solved for each dot pattern, resulting in processing overhead and a long running time. To overcome this restriction, parallel computing techniques are used to decrease the processing time. More specifically, the method is tested using a CUDA-based parallel implementation, running on GPUs. The proposed technique produces results with high visual quality and acceptable processing time.     

A Survey of Electronic Techniques for Pictorial Image Reproduction

This paper is a tradeoff study of image processing algorithms that can be used to transform continuous tone and halftone pictorial image input into spatially encoded representations compatible with binary output processes. A large percentage of the electronic output marking processes utilize a binary mode of operation. The history and rationale for this are reviewed and thus the economic justification for the tradeoff is presented. A set of image quality and processing complexity metrics are then defined. Next, a set of algorithms including fixed and adaptive thresholding, orthographic pictorial fonts, electronic screening, ordered dither, and error diffusion are defined and evaluated relative to their ability to reproduce continuous tone input. Finally, these algorithms, along with random nucleated halftoning, the alias reducing image enhancement system (ARIES), and a new algorithm, selective halftone rescreening (SHARE), are defined and evaluated as to their ability to reproduce halftone pictorial input.     

Adaptive-Hierarchical-Filtering Technique for High-Quality Magazine Image Reproduction

This paper proposes a novel adaptive filtering technique to achieve high-quality image enhancement when the image possesses the artifact of moiré pattern during the reproduction by different computer peripherals such as color copiers, or scanners plus printers.Commercial magazine images are halftoned images. Unacceptable noises and moiré distortion may occur when halftone images are copied (i.e., scanned and printed). In this paper, we analyze the formation of moiré patterns in both the frequency and spatial domain. Basically moiré noise often appears due to the aliased frequency when a halftone image is scanned. Based on the analysis of the scanned halftone image, we develop an adaptive filter to suppress the moiré artifacts and produce the high-quality image reproduction. The adaptive filter consists of modules of anti-aliased filter and image enhance filter: the anti-aliased filter is applied to cancel aliased low frequency components (moiré distortion); the image enhance filter is applied to sharpen image edges. It depends on the information provided by an image classification module to decide either the anti-aliased or image enhance module should be applied. The classification module is developed based on a set of pyramid images to determine an edge is either a global true edge (for sharpening enhancement) or a local halftones micro-structural edge (for moiré reduction). Depending on the information from the classification module, the adaptive filter technique then applies the anti-aliased filter to the halftone micro-structured edge or the enhanced filter to the image global edge correctly and efficiently, and therefore both the moiré reduction and image enhancement can be achieved simultaneously. Experimental results show the outstanding effectiveness of the presented technique for high-quality magazine image reproduction.Joseph Shu was born in Taiwan, Republic of China in 1954. He received his B.S. degree from Communication Engineering department, National Chiao-Tung University, Taiwan, R.O.C. in 1978, M.S. degree in 1984 and Ph.D. degree in 1986 both in Electric, Computer, and Systems Engineering department, Rensselaer Polytechnic Institute, Troy, New York. From 1986 to 1990, Dr. Shu worked for NYNEX Corporation. From 1990 to 1991, he worked in Hewlett-Packard Labs. Since 1991, he has been working for Epson. He is now a consultant scientist for Epson Palo Alto Lab. He has 35 U.S. patents. He has published over 25 technical papers in image segmentation, enhancement, restoration, analysis, moire reduction, pattern recognition, color processing and halftoning.Tsung-Nan Lin received B.S. degree in electrical engineering from National Taiwan University, Taiwan, R.O.C. in 1989, and M.A. and Ph.D. degrees from Princeton University in 1993 and 1996, respectively, both in electrical engineering department. He was a Teaching Assistant with the Department of Electrical Engineering from 1991 to 1992. Hewas with NEC Research Institute as a Research Assistant from 1992 to 1996. He has been with EPSON R&D Inc and EMC. Since Feb. 2002, he has been with Department of Electrical Engineering and Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan. Tsung-Nan Lin is a member of PHI TAUPHI scholastic honor society and a senior member of IEEE.     

Enhancing inverse halftoning via coupled dictionary training

Inverse halftoning is a challenging problem in image processing. Traditionally, this operation is known to introduce visible distortions into reconstructed images. This paper presents a learning-based method that performs a quality enhancement procedure on images reconstructed using inverse halftoning algorithms. The proposed method is implemented using a coupled dictionary learning algorithm, which is based on a patchwise sparse representation. Specifically, the training is performed using image pairs composed by images restored using an inverse halftoning algorithm and their corresponding originals. The learning model, which is based on a sparse representation of these images, is used to construct two dictionaries. One of these dictionaries represents the original images and the other dictionary represents the distorted images. Using these dictionaries, the method generates images with a smaller number of distortions than what is produced by regular inverse halftone algorithms. Experimental results show that images generated by the proposed method have a high quality, with less chromatic aberrations, blur, and white noise distortions.     

A new framework for characterization of halftone textures.

Characterization of halftone texture is important for quantitative assessment of halftone quality. In this paper, we develop a new framework based on directional local sequency analysis and a filter bank structure. We decompose a halftone image into subband images, from which we can easily reconstruct the original halftone. Based on these subband images, we define the directional sequency spectrum which is analogous to the two-dimensional Fourier spectrum, and formulate several texture measures. Two test image sets are used to justify these measures.     

Digital color halftoning

Digital color halftoning is the process of transforming continuous-tone color images into images with a limited number of colors. The importance of this process arises from the fact that many color imaging systems use output devices such as color printers and low-bit depth displays that are bilevel or multilevel with a few levels. The goal is to create the perception of a continuous-tone color image using the limited spatiochromatic discrimination capability of the human visual system. In decreasing order of how locally algorithms transform a given image into a halftone and, therefore, in increasing order of computational complexity and halftone quality, monochrome digital halftoning algorithms can be placed in one of three categories: 1) point processes (screening or dithering), 2) neighborhood algorithms (error diffusion), and 3) iterative methods. All three of these algorithm classes can be generalized to digital color halftoning with some modifications. For an in-depth discussion of monochrome halftoning algorithms, the reader is directed to the July 2003 issue of IEEE Signal Processing Magazine. In the remainder of this article, we only address those aspects of halftoning that specifically have to do with color. For a good overview of digital color halftoning, the reader is directed to Haines et al. (2003). In addition, Agar et al. (2003) contains a more in-depth treatment of some of the material found in this work.