Accurate color reproduction is important in many applications of 3D printing, from design prototypes to 3D color copies or portraits. Although full color is available via other technologies, multi-jet printers have greater potential for graphical 3D printing, in terms of reproducing complex appearance properties. However, to date these printers cannot produce full color, and doing so poses substantial technical challenges, from the shear amount of data to the translucency of the available color materials. In this article, we propose an error diffusion halftoning approach to achieve full color with multi-jet printers, which operates on multiple isosurfaces or layers within the object. We propose a novel traversal algorithm for voxel surfaces, which allows the transfer of existing error diffusion algorithms from 2D printing. The resulting prints faithfully reproduce colors, color gradients and fine-scale details.
One category of algorithms, called , allow a very fast computation of the halftone screens by thresholding pixels using a precomputed threshold mask that is tiled over the 2D image. The traditional clustered-dot order dithering to create amplitude modulated screens and dispersed-dot ordered dithering @cite_21 fall into this category. The latter technique was adapted to 3D printing @cite_40 accounting particularly for dot placement limitations of binder-jetting systems. One drawback of dispersed-dot ordered dithering is that frequency components given by the screen period are visible resulting in cross-hatch pattern artifacts. Avoiding such artifacts in point process techniques, requires large threshold masks -- e.g. blue-noise masks @cite_15 or green-noise masks @cite_32 -- which are heavily distorted if applied on surface manifolds with non-zero Gaussian curvature.
@cite_40 @cite_21 @cite_32 @cite_15
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We introduce a novel technique for generating green-noise halftones—stochastic dither patterns composed of homogeneously distributed pixel clusters. Although techniques employing error diffusion have been proposed previously, the technique here employs a dither array referred to as a green-noise mask, which greatly reduces the computational complexity formerly associated with green noise. Compared with those generated with blue-noise masks, halftones generated with green-noise masks are less susceptible to printer distortions. Because green noise constitutes patterns with widely varying cluster sizes and shapes, the technique introduced here for constructing these green-noise masks is tunable; that is, it allows for specific printer traits, with small clusters reserved for printers with low distortion and large clusters reserved for printers with high distortion. Given that blue noise is a limiting case of green noise, this new technique can even create blue-noise masks. Introduction AM Digital Halftoning FM Digital Halftoning AM-FM Hybrids AM Halftoning Dot Shape Screen Angles and Moire Screen Frequency Supercells Zero-Angle Dither Arrays Stochastic Halftone Analysis Point Processes Spatial Statistics Spectral Statistics Color Halftoning Halftone Visibility Campbell's CSF Model Nasanen (Exponential) Model Mixed Gaussian Models Alpha Stable HVS Models Blue-Noise Dithering Spatial and Spectral Characteristics Error Diffusion Blue-Noise Dither Arrays Simulated Annealing Void and Cluster BIPPSMA Dither Pattern Ordering Direct Binary Search Halftoning by DBS Efficient Implementation of DBS Effect of HVS model Hexagonal Grid Halftoning Spectral Aliasing Modified Blue-Noise Model Hexagonal Sampling Grids Printers: Distortions and Models Printer Distortion Dot Models Corrective Measures Green-Noise Dithering Spatial and Spectral Characteristics EDODF Green-Noise Masks BIPPCCA Optimal Green-Noise Masks Color Printing Generalized Error Diffusion Multichannel Green-Noise Masks Stochastic Moire Spatial Analysis of Periodic Moire Spatial Analysis of Aperiodic Moire Spectral Analysis of Aperiodic Moire Minimizing Stochastic Moire Stochastic Moire and Green-Noise Multitone Dithering Spectral Statistics of Multitones Multitone Blue-Noise Model Blue-Noise Multitoning Optimization Lenticular Halftoning Model Based Error Diffusion Iterative Tone Correction Conclusions Bibliography List of Figures Index A novel digital halftoning technique, by which the halftoning is achieved by a pixelwise comparison of the gray-scale image to an array (halftone screen), the blue-noise mask, is presented. This mask is designed so that the halftone image has blue-noise (high-frequency) characteristics in the frequency domain. The algorithm for the construction of the blue-noise mask and an algorithm for the construction of binary patterns with the same first-order but different second-order statistics are presented. Two psychovisual tests in which human subjects rated halftone patterns and images according to various criteria are also described. This paper presents a novel preprocessing method based on mathematical morphology techniques to improve the subsequent thresholding quality of raw degraded word images. The raw degraded word images contain undesirable shapes called critical shadows on the background that cause noise in binary images. This noise constitutes obstacles to posterior segmentation of characters. Direct application of a thresholding method produces inadequate binary versions of these degraded word images. Our preprocessing method called Shadow Location and Lightening (SL*L) adaptively, accurately and without manual fine-tuning of parameters locates these critical shadows on grayscale degraded images using morphological operations, and lightens them before applying eventual thresholding process. In this way, enhanced binary images without unpredictable and inappropriate noise can be provided to subsequent segmentation of characters. Then, adequate binary characters can be segmented and extracted as input data to optical character recognition (OCR) applications saving computational effort and increasing recognition rate. The proposed method is experimentally tested with a set of several raw degraded images extracted from real photos acquired by unsophisticated imaging systems. A qualitative analysis of experimental results led to conclusions that the thresholding result quality was significantly improved with the proposed preprocessing method. Also, a quantitative evaluation using a testing data of 1194 degraded word images showed the essentiality and effectiveness of the proposed preprocessing method to increase segmentation and recognition rates of their characters. Furthermore, an advantage of the proposed method is that Otsu's method as a simple and easily implementable global thresholding technique can be sufficient to reducing computational load.