Feature-preserving multiscale error diffusion for digital halftoning

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.

A digital halftoning method is proposed to diffuse error with a more symmetric error distribution by making use of the concept of multiscale error diffusion. It can improve the diffusion performance by effectively reducing directional hysteresis. The diffusion is row-oriented father than frame-oriented and hence can reduce a lot of computation effort as compared with conventional multiscale error diffusion schemes. This makes it possible for real-time applications.

A digital halftoning method is proposed to diffuse error with a more symmetric error distribution by making use of the concept of multiscale error diffusion. The method can improve the diffusion performance by effectively reducing directional hysteresis. The diffusion is row-oriented rather than frame-oriented and hence can reduce the latency and computational effort as compared with conventional multiscale error diffusion schemes. This makes it possible for real-time applications.

Taking colorimetric color reproduction into account, the conventional error diffusion method is modified for color digital halftoning. Assuming that the input to a bilevel color printer is given in CIE-XYZ tristimulus values or CIE-LAB values instead of the more conventional RGB or YMC values, two modified versions based on vector operation in (1) the XYZ color space and (2) the LAB color space were tested. Experimental results show that the modified methods, especially the method using the LAB color space, resulted in better color reproduction performance than the conventional methods. Spatial artifacts that appear in the modified methods are presented and analyzed. It is also shown that the modified method (2) with a thresholding technique achieves a good spatial image quality.

Color halftoning is a method for representing continuous tone color images with limited colors. To implement halftone, error diffusion is one of the most popular algorithms due to its highperformance results. In the conventional error diffusion, the fixed thresholds and filter weights are used. In the process, many artifacts are introduced in the halftone images as side effects. With the color aspect, each of the three color planes is independently calculated during the process of traditional error diffusion. However, the pattern of dots formed independently by combining the dots of each of color planes is often not visually pleasing because no effort is made to insure that the dots of each of the different color planes are ideally distributed relative to the dots of the other color planes. In this paper, we present an integrated error diffusion algorithm in which variable filter weights, threshold matrix, serpentine scan path and multi-color halftoning are used to reduce the noises generated during the halftone process. Our experimental results showed that worm-like artifacts, structural artifacts and start-up artifacts are successfully decreased and the color halftoned images are more comfortable to the human visual system.

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.

In Digital Subtraction Angiography (DSA) image registration algorithm, the precision of the control points as well as their number and the distribution in image determine the accuracy of geometric correction and registration. Control points usually adopt the grid points; however, a more effective method is to extract control points adaptively according to the image feature. In this paper, a control point’s selection algorithm of DSA images is proposed based on adaptive multi-Scale vascular enhancement, error diffusion and means shift algorithms. Experimental results show that the proposed algorithm can adaptively put the control points to blood vessels and other key image characteristics, and can optimize the number of control points according to practical needs, which will ensure the accuracy of DSA image registration.

The error diffusion method can effectively reduce quality degradation by propagating thresholding errors to neighboring pixels in the conversion of a gray-scale hologram to a binary hologram. In previous works, the four weighting coefficients in error diffusion are mostly set as the Floyd-Steinberg coefficient, which was determined empirically and originally proposed for photograph processing. In this work, we point out that the Floyd-Steinberg coefficients can be suboptimal for hologram error diffusion binarization. Furthermore, the weighting coefficients are optimized for each different hologram adaptively. Compared with conventional coefficients, our optimized coefficients can better preserve the fidelity of a reconstructed image after a hologram is binarized.

Aninnovative digital half toning procedure is established on multiscale error diffusion is studied in this paper. In this research a negative multiscale error diffusion technique is proposed having more practical significance. The prominent features of the algorithm are (1) Application of image quadtree (2) Selection of the darkest pixel based on minimum intensity guidance theorem (5) Non causal diffusion of error. The quality of the halftoned image is evaluated both through subjective and objective evaluations. For the objective evaluation feature similarity index is adopted in this paper. The proposed technique provides enhanced result both visually and in terms of feature similarity index.

Advances in printing technology has made multilevel halftoning become important, as printers can now print inks of different intensities. This work presents a multilevel halftoning algorithm based on multiscale error diffusion. This algorithm takes care of constrained pixels before handling the unconstrained pixels, and diffuses errors with a noncausal filter such that halftones of better image quality can be achieved.

Among the digital halftoning, the error diffusion halftoning gives better subjective quality than other halftoning techniques. But it also makes edges of objects blurred. To overcome the defect, this thesis proposes the modified error diffusion halftoning algorithm to enhance the edges using the spatial perceptual properties of the human visual system. Using the properties that the human eyes perceive not the pixel's luminance itself but the local average luminance and that information that human eyes perceive spatial variation, the proposed method computes information of edge enhancement (IEE). The IEE is added to the quantizer's input pixel and feeds into the halftoning quantizer. The quantizer produces the halftone image having the enhanced edge. Also this thesis proposes the technique that the coefficients of the error diffusion filter are adapted according to the correlation among color components. The computer simulation results show that the proposed method produces finer halftoning images than conventional methods due to the enhanced edges. And the proposed method also preserves similar in edges to original image and reduces some defects such as color impulse and false contours.

In person re-identification, extracting image features is an important step when retrieving pedestrian images. Most of the current methods only extract global features or local features of pedestrian images. Some inconspicuous details are easily ignored when learning image features, which is not efficient or robust to for scenarios with large differences. In this paper, we propose a Multi-level Feature Fusion model that combines both global features and local features of images through deep learning networks to generate more discriminative pedestrian descriptors. Specifically, we extract local features from different depths of network by the Part-based Multi-level Net to fuse low-to-high level local features of pedestrian images. Global-Local Branches are used to extract the local features and global features at the highest level. The experiments have proved that our deep learning model based on multi-level feature fusion works well in person re-identification. The overall results outperform the state of the art with considerable margins on three widely-used datasets. For instance, we achieve 96% Rank-1 accuracy on the Market-1501 dataset and 76.1% mAP on the DukeMTMC-reID dataset, outperforming the existing works by a large margin (more than 6%).

Reproduction couleur par trames irrégulières et semi-régulières

Dot diffusion is an efficient approach which utilizes concepts of block-wise and parallel-oriented processing to generate halftones. However, the block-wise nature of processing reduces image quality much more significantly as compared to error diffusion. In this work, four types of filters with various sizes are employed in co-optimization procedures with class matrices of size 8 n 8 and 16 x 16 to improve the image quality. The optimal diffused weighting and area are determined through simulations. Many well-known halftoning methods, some of which includes direct binary search (DBS), error diffusion, ordered dithering, and prior dot diffusion methods, are also included for comparisons. Experimental results show that the proposed dot diffusion achieved quality close to some forms of error diffusion, and additionally, superior to the well-known Jarvis and Stucki error diffusion and Mese's dot diffusion. Moreover, the inherent parallel processing advantage of dot diffusion is preserved, allowing us to reap higher executing efficiency than both DBS and error diffusion.

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.