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.

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.

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

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Abstract. Multiscale error diffusion (MED) is superior to conventional error diffusion algorithms as it