A good halftoning output should bear a blue noise characteristic contributed by isotropically-distributed isolated dots. Multiscale error diffusion (MED) algorithms try to achieve this by exploiting radially symmetric and noncausal error diffusion filters to guarantee spatial homogeneity. In this brief, an optimized diffusion filter is suggested to make the diffusion close to isotropic. When it is used with MED, the resulting output has a nearly ideal blue noise characteristic.

Multiscale error diffusion (MED) is superior to conventional error diffusion algorithm as it can eliminate directional hysteresis completely and possesses good blue noise characteristics. However, due to its filter design, it is not suitable for printing processes which suffer instable dot generation and large dot gain. This paper presents a feature preserving multiscale error diffusion algorithm for green noise digital halftoning. To cope with the edge of the image, the selected diffusion filter is modified according to the edge orientation. Spatial statistics analysis and simulation results show that the proposed algorithm does not only provide outputs of green noise characteristics, but preserve edge details and eliminate pattern artifacts and directional artifacts as well.

Multiscale error diffusion (MED) is superior to conventional error diffusion algorithms as it can eliminate directional hysteresis completely and possesses a good blue noise characteristic. However, due to its filter design, it is not suitable for systems with poor isolated dot generation and instable dot gain. In this paper, we propose a MED algorithm to produce halftones of desirable green noise characteristics. This algorithm allows one to adjust the desirable cluster size freely through a single parameter and supports a linear relationship between the cluster size and the input gray level. With a close-to-isotropic diffusion filter, the algorithm can effectively remove pattern artifacts, eliminate directional artifacts and preserve original image details. Analysis and simulation results show that it provides better performance in terms of various aspects including dot distribution, anisotropy and output image quality as compared with other conventional green noise error diffusion algorithms.

Restoration of color-quantized images in which error diffusion is involved is rarely addressed in the literature. Conventional restoration algorithms were generally proposed for handling other noise models and hence they are not effective in restoring color-quantized images. In this paper, we tackle this problem by making use of the best linear estimation. It is shown by the simulation results that the algorithm can improve the quality of color-quantized images to a certain extent

To efficiently render halftone images for various printers and displays that support different resolutions, it is desirable that all halftoning results can be produced at a time, and all of them can be embedded in a single full-scale halftone image such that a simple down-sampling process can extract images of suitable resolutions from it if necessary. This letter presents a framework for achieving this objective with multiscale error diffusion. A multi-resolution halftoning algorithm is proposed based on this framework. Simulation results show that the proposed algorithm can provide good image quality at different resolutions.

This paper studies the restoration of images which are color-quantized with error diffusion. Though there are many reported algorithms proposed for restoring noisy blurred color images and inverse halftoning, restoration of color-quantized images is rarely addressed in the literature especially when the images are color-quantized with halftoning. Direct application of existing restoration techniques are generally inadequate to deal with this problem. In this paper, a restoration algorithm based on projection onto convex sets is proposed. This algorithm makes use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. Simulation results showed that it could improve the quality of a halftoned color-quantized image remarkably in terms of both SNR and CIELAB color difference metric.

To reliably and efficiently deliver media information to diverse clients over heterogeneous networks, the media involved must be scalable. In this paper, a color quantization algorithm for generating scalable color-indexed images is proposed based on a multiscale error diffusion framework. Images of lower resolutions are embedded in the outputs such that a simple down-sampling process can extract images of any desirable resolutions. Images possessing this scalable property support transmission over the Internet which contains clients with different display resolutions, systems with different caching resources and networks with varying bandwidths and QoS capabilities. Unlike most of the color halftoning algorithms available nowadays, the proposed algorithm is not dedicated for printing applications but for color-indexed displays. It works with any arbitrary palettes of different size.

Restoration of color-quantized images is rarely addressed in the literature, especially when the images are color-quantized with halftoning. Direct application of existing restoration techniques are generally inadequate to deal with this problem. We propose a POCS-based restoration algorithm to solve the problem. This algorithm derives useful information for restoration with the available color palette and the mechanism of a halftoning process. It is shown to be able to provide a good restoration result.

Restoration of color-quantized images is rarely addressed in the literature, especially when the images are color-quantized with halftoning. Direct applications of existing restoration techniques are generally inadequate to deal with this problem. A restoration algorithm based on simulated annealing is proposed to solve the problem. This algorithm makes a good use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. It is shown to be able to provide a good restoration result.

In fractal coding technique, an image is encoded by making use of its self-similarity property. The image can be reconstructed with some well-defined contractive mappings based on this property and hence, in theory, the reconstructed image can be of any desirable size by using an initial image of appropriate size during the decoding process. However, in practice, the enlarged image is always degraded due to the sub-optimal contractive mappings used. In this paper, a fractal-based image enlargement technique is proposed to reduce this problem. This technique can preserve the details in edge regions while maintaining the smoothness in flat regions, which is superior to conventional image enlargement techniques such as bilinear interpolation and cubic convolution.

This paper studies the restoration of color-quantized images. Restoration of color-quantized images is rarely addressed in the literature, and direct applications of existing restoration techniques are generally inadequate to deal with this problem. We propose a POCS-based restoration algorithm specific to color-quantized images, which makes a good use of the available color palette to derive useful a priori information for restoration. The proposed restoration algorithm is shown to be capable of improving the quality of a color-quantized image to a certain extent.

An algorithm for removing impulse noise is proposed. The algorithm not only uses the information of neighboring pixels in a local region, but also uses the long-range correlation within a natural image. In the proposed algorithm, a search criterion based on the weighted transformed contents of regions is used to look for a region which is highly correlated to the region of interest and then the center of that region is selected to replace the corrupted pixel. The method is found to be very effective in removing impulse noise from corrupted images, both in terms of the objective distortion measure and subjective visual assessment.