An Integrated Color Halftone Method for Noise Reduction

This paper proposes an adaptive error diffusion algorithm for text enhancement followed by an efficient text segmentation that uses the maximum gradient difference (MGD). The gradients are calculated along with scan lines, then the MGD values are filled within a local window to merge text segments. If the value is above a threshold, the pixel is considered as potential text. Isolated segments are then eliminated in a non-text region filtering process. After the text segmentation, a conventional error diffusion method is applied to the background, while edge enhancement error diffusion is used for the text. Since it is inevitable that visually objectionable artifacts are generated when using two different halftoning algorithms, gradual dilation is proposed to minimize the boundary artifacts in the segmented text blocks before halftoning. Sharpening based on the gradually dilated text region (GDTR) then prevents the printing of successive dots around the text region boundaries. The method is extended to halftone color images to sharpen the text regions. The proposed adaptive error diffusion algorithm involves color halftoning that controls the amount of edge enhancement using a general error filter. However, edge enhancement unfortunately produces color distortion, as edge enhancement and color difference are trade-offs. The multiplicative edge enhancement parameters are selected based on the amount of edge sharpening and color difference. Plus, an additional error factor is introduced to reduce the dot elimination artifact generated by the edge enhancement error diffusion. In experiments, the text of a scanned image was sharper when using the proposed algorithm than with conventional error diffusion without changing the background.

Multiscale error diffusion is superior to conventional error diffusion methods in digital halftoning as it can eliminate directional hysteresis completely. However, there is a bias to favor a particular type of dots in the course of the halftoning process. A new multiscale error diffusion method is proposed to improve the diffusion performance by reducing the aforementioned bias. The proposed method can eliminate the pattern noise in flat regions and the boundary effect found in some other conventional multiscale error diffusion methods. At the same time, it can preserve the local features of the input image in the output. This is critical to quality, especially when the resolution of the output is limited by the physical constraints of the display unit.

Inverse color to black-and-white halftone conversion via dictionary learning and color mapping

Reproduction couleur par trames irrégulières et semi-régulières

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 least-mean-square is also introduced. In the encoder, the kernels-alternated error diffusion (KAEDF) is applied. This is able to maintain the computational complexity at the same level as ordinary error diffusion. Compared with Hel-Or (2001) 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 LUT, as proposed by Pei and Guo (2003), so as to achieve a higher decoded rate. As the experimental results demonstrated, this technique is able to guard against degradation due to tampering, cropping, rotation, as well as print-and-scan processes in error-diffused halftone images.

In this paper, a low computational complexity method is proposed to embed a multi-tone visual pattern using the concept of secret sharing. One halftone image is obtained using regular error diffusion, and the other image is obtained using the proposed Generalized Noise-Balanced Error Diffusion (GNBEDF) in considering the properties of the watermark and the first halftone image in the same time. With the proposed method, the bi-level watermark embedding becomes a special case to this technology. The bi-level watermark decoding can be simply achieved via printing the two halftone images onto two transparencies and then superimposing them together to reveal the watermark pattern. However, as the multi-tone watermark is involved, the proposed Gaussian Difference (GD) approach is needed to perform the decoding to produce a decoded multi-tone watermark. Throughout this work, a lowpass filter obtained by Least-Mean-Square is employed to assess the image quality.

A dot-to-dot error diffusion algorithm is presented that combines traditional halftoning (clustered-dot ordered dithering) and error diffusion. Error diffusion can create a large number of nonprintable individualpixels. Therefore, it is not suitable forhard-copy printing. On the other hand, traditional halftoning has only limited output gray levels. With the algorithm we proposed, we try to avoid the problems of both techniques, yet retain their merits. In our algorithm, the error diffusion is performed on halftone dots instead of pixels, and the error diffusion is performed only on full dots. To accommodate the dot-to-dot error propagation, a modified but equivalent version of error diffusion is introduced. The success of the algorithm is demonstrated by the experimental results.

The objective of this work is to reconstruct high quality gray-level images from halftone images, or the inverse halftoning process. We develop high performance halftone reconstruction methods for several commonly used halftone techniques. For better reconstruction quality, image classification based on halftone techniques is placed before the reconstruction process so that the halftone reconstruction process can be fine tuned for each halftone technique. The classification is based on enhanced 1D correlation of halftone images and processed with a three- layer back propagation neural network. This classification method reached 100 percent accuracy with a limited set of images processed by dispersed-dot ordered dithering, clustered-dot ordered dithering, constrained average, and error diffusion methods in our experiments. For image reconstruction, we apply the least-mean-square adaptive filtering algorithm which intends to discover the optimal filter weights and the mask shapes. As a result, it yields very good reconstruction image quality. The error diffusion yields the best reconstructed quality among the halftone methods. In addition, the LMS method generates optimal image masks which are significantly different for each halftone method. These optimal masks can also be applied to more sophisticated reconstruction methods as the default filter masks.

Digital halftoning is essential to print or display a continuous tone with binary devices. Error Diffusion (ED) has been a major stream to produce a pictorial image on inkjet prints, but is not always acceptable for commercial printing or EP printer, because it generates the dispersed dots hard to print.This paper proposes a novel method for generating ordered dither mask using “magic square”. Magic square provides spatially balanced threshold values in row, column, and diagonal directions by the rule of “even-sum”. The proposed magic square satisfies the energy minimum criterion in the local area and is expandable to a large scale for sufficient gray scales. We selected the magic square candidates with Fourier spectra concentrated in the middle spatial frequency to produce a smoothed gradation suitable for LBP or commercial printing. The paper introduces a systematic method for expanding small mask to large mask by “master-slave” algorithm. The experimental results show the improved grayscale reproduction with reduced artifacts in comparison with the conventional Bayer dither or ED.

We have previously developed a two-dimensional monochromatic halftoning with error diffusion neural networks (EDNs). We showed that EDNs find local minima of frequency-weighted error between a binary halftone output and corresponding smoothly varying input, an ideal framework for solving halftone problems. The advantage of the neural algorithm is that all pixel quantization decisions are computed in parallel and therefore the error diffusion process becomes un-directional and symmetric. Thus eliminates the artifacts caused by the traditional error diffusion method. This paper casts color halftoning as four related sub-problems: the first three are to compute good binary halftones for each primary color and the fourth is to simultaneously minimize frequency-weighted error in the luminosity of the composite result. We show that an EDN with a three-dimensional interconnect scheme can solve all four problems in parallel with user-adjustable emphasis on the relative importance of weighted error in luminosity. Our results show that the three-dimensional EDN matrix not only shapes the error to frequencies that human visual system (HVS) is least sensitive but also shapes the error in colors to which the HVS is least sensitive - namely it satisfies the minimum brightness variation criterion.

Halftoning is a process that deliberately injects noise into the original image in order to obtain visually pleasing output images with a smaller number of bits per pixel for displaying or printing purposes. In this paper, a novel inverse halftoning method is proposed to restore a continuous tone image from the given halftone image. A set theoretic formulation is used where three sets are defined using the prior information about the problem. A new space domain projection is introduced assuming the halftoning is performed with error diffusion, and the error diffusion filter kernel is known. The space domain, frequency domain, and space-scale domain projections are used alternately to obtain a feasible solution for the inverse halftoning problem which does not have a unique solution.

A new halftoning algorithm is proposed that incorporates the concept of delayed decision in error diffusion. This algorithm is similar to a recently proposed tree coding halftoner in that both examine at each pixel location a neighborhood of future (with respect to the scanning direction) pixels before deciding the value of the current output pixel. Because of the feedback in the error diffusion process, one can reduce the size of the 'look ahead' window compared to a typical tree-coding halftoner, resulting in a relatively low complexity. It is shown that this algorithm improves the quality of halftone images compared to those generated by traditional error diffusion.

Image data hiding is the hiding of invisible patterns in an image without degrading its visual quality. The hidden pattern can be visualized by overlaying the original image with watermarked ones or by applying an extraction algorithm. If the hidden pattern is visualized using the overlaying method, it can also be visualized using a simple XNOR operation. In the case that hidden data is extracted by a specific algorithm, it is commonly called steganography. In this paper we propose a method called dot diffusion with nonlinear thresholding (DDNT) to embed hidden data in halftone images, where the hidden data can be extracted by overlaying original and watermarked images or by performing the XNOR process on these two images. The main advantage of this method is that the hidden pattern's intensity can be adjusted by three parameters. The DDNT data hiding method utilizes the inherent parallelism in dot diffusion halftoning method, although it suffers from its lower visual quality than some other halftoning methods such as error diffusion

We present an algorithm for image browsing systems that embeds a color halftone into another color halftone of a higher bit depth, as well as embedding a grayscale or binary halftone into a grayscale or color halftone. The benefit of this algorithm is that a low bit-depth halftone can be directly obtained from a higher bit-depth halftone for printing of progressive transmission simply by masking one or more bits off of the color halftone. The embedding can be done in any bit of the output depending on the organization of the image palettes, although the most significant or the least significant bits are most convenient. The embedding algorithm that we describe requires the use of scalar quantizers and ordered vector quantizers. We present several methods for color palette organization, or binary index assignment, to be used in conjunction with the embedded color error diffusion algorithm.

The process of conversion of continuous tone image to discrete dots is called halftoning. The existing halftoning techniques are not suitable for all types of images. Hence, in this paper, the contrast of color halftone image is enhanced using the proposed error diffusion with varying threshold (EDVT) halftoning. This algorithm is the combination of point processing and neighborhood processing halftoning techniques. EDVT color halftoning varies the threshold for each block of pixels, and also it diffuses the quantization error to the neighboring pixels. The simulation results have been carried out for color halftone images using EDVT halftoning algorithm, and it is analyzed with the quality parameters which reveals that it has improved quality than the existing halftoning techniques.