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Conversion of Resolution and Intensity using Cellular Neural Networks

使用细胞神经网络转换分辨率和强度

基本信息

批准号：
10650379
负责人：
TANAKA Mamoru
金额：
$ 1.41万
依托单位：
SOPHIA UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10650379/
关键词：
Cellular Neural Network Retina Information Image Quantization Energy Minimization Conversion of Intensity

项目摘要

The spatio-temporal dynamics by CNN(Cellular Neural Network) must generate nonlinear interpolative effects which have not been generated by a conventional image processing by sequential machine. In this research, a high quality secondary color image can be generated by using the CNN spatio-temporal dynamics for any resolution for both of a dense gradation image and an area intensity image. The dense gradation image is got from the area intensity image generated from the spatio-temporal dynamics of the CNN.Each cell of the CNN controls on-off state of the corresponding area element in pixel and then the intensity of each pixel can be controlled by changing the number of area elements per each pixel. That is, multi-value intensity can be generated by only using a set of 1-bit cells in the universal CNN.The number of bits per pixel for color density should be controlled efficiently by the number of pixels for a given input resolution because high density is required for low resolution and low density is enough for high resolution. This auto-resolution is very useful from practical viewpoints.The CNN can be used efficiently for the auto-resolotion because the number of inner cells in each pixel is controlled automatically, even if the number of quantized levels of inner cell is only 2 (binary). The problem is how the color density per pixel can be generated by using spatio-temporal dynamics by a set of inner cells in each pixel. The problem can be solved based on a conversion from spatial area intensity process to density process in each pixel. The CNN for auto-resolotion has hierarchical structure in which a global CNN consists of cells corresponding to the number of pixels in an image and a local CNN consists of real inner cells. The discrete time CNN has been designed as a chip by using Parthenon high level language developed by NTT.

细胞神经网络（CNN）的时空动态处理必须产生非线性插值效应，这是传统序列机图像处理所不能产生的。在本研究中，对于密集渐变图像和面积强度图像，在任何分辨率下，使用CNN时空动态都可以生成高质量的二次彩色图像。由CNN的时空动态生成的面积强度图像得到密集的渐变图像。CNN的每个cell控制像素中对应的区域元素的通断状态，然后通过改变每个像素的区域元素的数量来控制每个像素的强度。即在通用CNN中仅使用一组1位单元格就可以生成多值强度。颜色密度的每像素位数应该通过给定输入分辨率的像素数有效地控制，因为低分辨率需要高密度，而高分辨率则需要低密度。从实际的角度来看，这种自动分辨率非常有用。CNN可以有效地用于自动分辨率，因为每个像素的内细胞数量是自动控制的，即使内细胞的量化级别只有2（二进制）。问题是如何通过每个像素中的一组内部单元使用时空动态来生成每个像素的颜色密度。该问题可以通过将空间面积强度过程转换为每个像素的密度过程来解决。用于自分辨的CNN具有层次结构，其中全局CNN由与图像像素数相对应的单元组成，局部CNN由真实的内部单元组成。离散时间CNN采用NTT公司开发的帕台农高级语言设计为芯片。