Image halftoning with cellular neural networks
Image halftoning with cellular neural networks
复制标题
使用细胞神经网络进行图像半色调
DOI：
10.1109/82.224318
复制
发表时间：
1993
期刊：
影响因子：
--
通讯作者：
L. Chua
中科院分区：
文献类型：
--
作者：
K. R. Crounse;T. Roska;L. Chua
在一个实际的半色调应用中使用神经网络的可行性被认为是。选择细胞神经网络（CNN）架构是因为其在VLSI和高速操作中的可实现性。由于CNN和半色调都具有几何局部特征，因此CNN提供了自然的实现。CNN模板权重通过类比于用于半色调化的众所周知的误差扩散算法而导出。分析了神经网络方法的局限性，提出了一种改进的模板权值设计方法。这些限制被证明是特别关键的情况下，需要高效的实施小的互连邻里。通过直接仿真验证了设计准则。由此产生的半色调被证明是更忠实的复制品的原始比那些产生的误差扩散算法。建议具有光学输入的CNN可以为传真等应用提供高速扫描器/半色调器。>
The feasibility of using neural networks in a practical halftoning application is considered. The cellular neural network (CNN) architecture is chosen for its proven implementability in VLSI and high-speed operation. Since both the CNN and halftoning have a geometrically local character, the CNN provides a natural implementation. The CNN template weights are derived by analogy to the well-known error diffusion algorithm for halftoning. Some limitations of the neural network approach are analyzed, providing an advance in designing template weights over previous methods. These limitations are shown to be especially critical in the case of the small interconnection neighbourhoods needed for efficient implementation. The design criteria are validated by direct simulation. The resulting halftones are shown to be more faithful reproductions of the original than those produced by the error diffusion algorithm. It is suggested that a CNN with optical inputs could provide a high-speed scanner/halftoner for applications such as facsimile. >