A fast, high-quality inverse halftoning algorithm for errordiffused halftones

Halftones and other binary images are difficult to process with causing several degradation. Degradation is greatly reduced if the halftone is inverse halftoned (converted to grayscale) before scaling, sharpening, rotating, or other processing. For error diffused halftones, we present (1) a fast inverse halftoning algorithm and (2) a new multiscale gradient estimator. The inverse halftoning algorithm is based on anisotropic diffusion. It uses the new multiscale gradient estimator to vary the tradeoff between spatial resolution and grayscale resolution at each pixel to obtain a sharp image with a low perceived noise level. Because the algorithm requires fewer than 300 arithmetic operations per pixel and processes 7x7 neighborhoods of halftone pixels, it is well suited for implementation in VLSI and embedded software. We compare the implementation cost, peak signal to noise ratio, and visual quality with other inverse halftoning algorithms.     

Towards optimized binary pattern generation for grayscale digital halftoning: A binary particle swarm optimization (BPSO) approach

Grayscale digital halftoning produces bi-level representation of original continuous tone images. This process plays pivotal role for devices like printers, plasma panels, LCD displays, etc. The bi-level images can be considered as binary images where ‘0’ and ‘1’ correspond to black and white, respectively. This paper investigates potential of binary particle swarm optimization (BPSO) to generate faithful binary halftone patterns. The cost function addresses important characteristics of original images and pleasant visual appearance of halftone images. The paper also shows the application of pattern look-up-table (p-LUT) approach to address the high processing time of BPSO optimization and simple gradient-based edge enhancement for improved edge retention. Results are evaluated subjectively by statistical measures and psychovisual test. Results are evaluated objectively using image quality evaluation metrics as well. The comparisons with state-of-the-art techniques are also drawn. The evaluation results along with the comparisons show the competitive potential of the presented technique.     

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.     

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.     

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.     

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.     

Binary grayscale halftone pattern generation using binary artificial bee colony (bABC)

In grayscale context, digital halftoning can be considered as binary pattern generation task where 0- and 1-valued pixels represent black and white, respectively. This paper presents application of artificial bee colony (ABC) in its modified binary form called binary ABC (bABC) toward generation of optimized binary halftone patterns. The human visual system incorporated cost function formulation addresses the tonal and structural parity between the original and halftone images along with pleasant halftone appearance. A pattern look-up table (p-LUT) approach is investigated including edge enhancement in halftone images. The results are presented pictorially and evaluated objectively. Comparison between the presented method and established halftoning techniques is also drawn to judge the potential of the presented bABC generated p-LUT approach. The pictorial presentations as well as objective evaluation show that the presented method may be potentially useful in the field of digital halftoning.     

Inverse halftoning using binary permutation filters

The problem of reconstructing a continuous-tone image given its ordered dithered halftone or its error-diffused halftone image is considered. We develop a modular class of nonlinear filters that can reconstruct the continuous-tone information preserving image details and edges that provide important visual cues. The proposed nonlinear reconstruction algorithms, denoted as binary permutation filters, are based on the space and rank orderings of the halftone samples provided by the multiset permutation of the "on" pixels in a halftone observation window. For a given window size, we obtain a wide range of filters by varying the amount of space-rank ordering information utilized in the estimate. For image reconstructions from ordered dithered halftones, we develop periodically space-varying filters that can account for the periodical nature of the underlying screening process. A class of suboptimal but simpler space-invariant reconstruction filters are also proposed and tested. Constrained LMS type algorithms are employed for the design of reconstruction filters that minimize the reconstruction mean squared error. We present simulations showing that binary permutation filters are modular, robust to image source characteristics, and that they produce high visual quality image reconstruction.     

Model-based color halftoning using direct binary search.

In this paper, we develop a model-based color halftoning method using the direct binary search (DBS) algorithm. Our method strives to minimize the perceived error between the continuous tone original color image and the color halftone image. We exploit the differences in how the human viewers respond to luminance and chrominance information and use the total squared error in a luminance/chrominance based space as our metric. Starting with an initial halftone, we minimize this error metric using the DBS algorithm. Our method also incorporates a measurement based color printer dot interaction model to prevent the artifacts due to dot overlap and to improve color texture quality. We calibrate our halftoning algorithm to ensure accurate colorant distributions in resulting halftones. We present the color halftones which demonstrate the efficacy of our method.     

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.     

基于FPGA的红外图像处理算法的测试系统

为识别和提取红外图像中的有用信息,红外系统常采用各种算法处理采集到的数据。随着红外技术的不断发展,与红外相关的图像处理算法层出不穷。除了常规的图像处理算法外,为了提高人眼对红外图像的识别率,伪彩色编码技术也常被使用。为了便于在实际应用选取最佳的红外图像处理算法,基于FPGA设计了红外图像处理算法的测试系统,该系统能集成与红外相关的常见图像处理算法和实时添加的自定义图像处理算法,并利用显示终端对红外图像数据进行实时验证,通过对彩虹编码、热金属编码与图像锐化算法的测试试验,表明了该测试系统的实用性。     

Image Segmentation Using Information Bottleneck Method

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms.     

Halftone image data hiding with intensity selection and connection selection

In this paper, we propose two novel algorithms, namely intensity selection (IS) and connection selection (CS), that can be applied to the existing halftone image data hiding algorithms DHSPT, DHPT and DHST to achieve improved visual quality. The proposed algorithms generalize the hidden data representation and select the best location out of a set of candidate locations for the application of DHSPT, DHPT or DHST. The two algorithms provide trade-off between visual quality and computational complexity. The IS yields higher visual quality but requires either the original multi-tone image or the inverse-halftoned image which implies high computation requirement. The CS has lower visual quality than IS but requires neither the original nor the inverse-halftoned images. Some objective visual quality measures are defined. Our experiments suggest that significant improvement in visual quality can be achieved, especially when the number of candidate locations is large.     

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.     

New single-parameter models for nonlinear electronic systems and its use in predicting intermodulation performance

Simple single-parameter models are presented for the instantaneous characteristics of nonlinear electronic systems. The models can easily provide closed-form expressions, in terms of the ordinary Bessel functions, for the amplitudes of the output components of the nonlinear systems excited by multisinusoidal signals. Moreover, being a single-parameter model, the nonlinear electronic circuit can be easily characterized using a set of measured data for the variation of the relative output third-order intermodulation over an appropriate range of input tone amplitudes of a two-tone equal-amplitude input signal.     

Entropy-constrained halftoning using multipath tree coding

We suggest an optimization-based method for halftoning that involves looking ahead before a decision for each binary output pixel is made. We first define a mixture distortion criterion that is a combination of a frequency-weighted mean square error (MSE) and a measure depending on the distances between minority pixels in the halftone. A tree-coding approach with the ML-algorithm is used for minimizing the distortion criterion to generate a halftone. While this approach generates halftones of high quality, these halftones are not very amenable to lossless compression. We introduce an entropy constraint into the cost function of the tree-coding algorithm that optimally trades off between image quality and compression performance in the output halftones.     

Continuous Phase-Modulated Halftones

A generalization of periodic clustered-dot halftones is proposed, wherein the phase of the halftone spots is modulated using a secondary signal. The process is accomplished by using an analytic halftone threshold function that allows halftones to be generated with controlled phase variation in different regions of the printed page. The method can also be used to modulate the screen frequency, albeit with additional constraints. Visible artifacts are minimized/eliminated by ensuring the continuity of the modulation in phase. Limitations and capabilities of the method are analyzed through a quantitative model. The technique can be exploited for two applications that are presented in this paper: a) embedding watermarks in the halftone image by encoding information in phase or in frequency and b) modulating the screen frequency according to the frequency content of the continuous tone image in order to improve spatial and tonal rendering. Experimental performance is demonstrated for both applications.      

Inverse halftoning and kernel estimation for error diffusion

Two different approaches in the inverse halftoning of error-diffused images are considered. The first approach uses linear filtering and statistical smoothing that reconstructs a gray-scale image from a given error-diffused image. The second approach can be viewed as a projection operation, where one assumes the error diffusion kernel is known, and finds a gray-scale image that will be halftoned into the same binary image. Two projection algorithms, viz., minimum mean square error (MMSE) projection and maximum a posteriori probability (MAP) projection, that differ on the way an inverse quantization step is performed, are developed. Among the filtering and the two projection algorithms, MAP projection provides the best performance for inverse halftoning. Using techniques from adaptive signal processing, we suggest a method for estimating the error diffusion kernel from the given halftone. This means that the projection algorithms can be applied in the inverse halftoning of any error-diffused image without requiring any a priori information on the error diffusion kernel. It is shown that the kernel estimation algorithm combined with MAP projection provide the same performance in inverse halftoning compared to the case where the error diffusion kernel is known.     

Impact of HVS models on model-based halftoning

A model for the human visual system (HVS) is an important component of many halftoning algorithms. Using the iterative direct binary search (DBS) algorithm, we compare the halftone texture quality provided by four different HVS models that have been reported in the literature. Choosing one HVS model as the best for DBS, we then develop an approximation to that model which significantly improves computational performance while minimally increasing the complexity of the code. By varying the parameters of this model, we find that it is possible to tune it to the gray level being rendered, and to thus yield superior halftone quality across the tone scale. We then develop a dual-metric DBS algorithm that effectively provides a tone-dependent HVS model without a large increase in computational complexity.     

Top down structured parallelisation of embedded image processingapplications

The authors present a general system design method which is intended to support parallelisation of complete image processing applications using MIMD processors. The approach is based upon the utilisation of a generic system level parallel processor architecture, the `pipeline processor farm'(PPF), and is applicable to any embedded application with continuous input/output. The design method is illustrated using applications from the fields of computer vision and image coding. The design model accommodates several commonly exploited parallel processing paradigms, maps conveniently to the software structure of most image processing algorithms, provides incrementally scalable performance, and enables upper-bound speedups to be easily estimated from profiling data generated by the original sequential implementation of the application. It is believed that the approach has significant application in parallel embedded systems design, in the development environment, and in simulation work for computationally intensive image coding algorithms