Bitwise operations of cellular automaton on gray-scale images

K. Mangalam, Prof. K S Venkatesh
{"title":"Bitwise operations of cellular automaton on gray-scale images","authors":"K. Mangalam, Prof. K S Venkatesh","doi":"10.1109/ISSC.2017.7983625","DOIUrl":null,"url":null,"abstract":"Cellular Automata (CA) theory is a discrete model that represents the state of each of its cells from a finite set of possible values which evolve in time according to a pre-defined set of transition rules. CA have been applied to a number of image processing tasks such as Convex Hull Detection, Image Denoising etc. but mostly under the limitation of restricting the input to binary images. In general, a gray-scale image may be converted to a number of different binary images which are finally recombined after CA operations on each of them individually. We have developed a multinomial regression based weighed summation method to recombine binary images for better performance of CA based Image Processing algorithms. The recombination algorithm is tested for the specific case of denoising Salt and Pepper Noise to test against standard benchmark algorithms such as the Median Filter for various images and noise levels. The results indicate several interesting invariances in the application of the CA, such as the particular noise realization and the choice of sub-sampling of pixels to determine recombination weights. Additionally, it appears that simpler algorithms for weight optimization which seek local minima work as effectively as those that seek global minima such as Simulated Annealing.","PeriodicalId":170320,"journal":{"name":"2017 28th Irish Signals and Systems Conference (ISSC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 28th Irish Signals and Systems Conference (ISSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSC.2017.7983625","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Cellular Automata (CA) theory is a discrete model that represents the state of each of its cells from a finite set of possible values which evolve in time according to a pre-defined set of transition rules. CA have been applied to a number of image processing tasks such as Convex Hull Detection, Image Denoising etc. but mostly under the limitation of restricting the input to binary images. In general, a gray-scale image may be converted to a number of different binary images which are finally recombined after CA operations on each of them individually. We have developed a multinomial regression based weighed summation method to recombine binary images for better performance of CA based Image Processing algorithms. The recombination algorithm is tested for the specific case of denoising Salt and Pepper Noise to test against standard benchmark algorithms such as the Median Filter for various images and noise levels. The results indicate several interesting invariances in the application of the CA, such as the particular noise realization and the choice of sub-sampling of pixels to determine recombination weights. Additionally, it appears that simpler algorithms for weight optimization which seek local minima work as effectively as those that seek global minima such as Simulated Annealing.
元胞自动机在灰度图像上的位运算
元胞自动机(CA)理论是一个离散模型,它从一组有限的可能值中表示每个细胞的状态,这些值根据一组预定义的过渡规则随时间演变。CA已经应用于许多图像处理任务,如凸壳检测、图像去噪等,但大多受限于将输入限制为二值图像。一般来说,灰度图像可以被转换成许多不同的二值图像,这些二值图像在分别对每个二值图像进行CA操作后,最终重新组合。为了提高基于CA的图像处理算法的性能,我们开发了一种基于多项回归的加权求和方法来重组二值图像。针对盐和胡椒噪声去噪的具体情况,对重组算法进行了测试,以测试不同图像和噪声水平的标准基准算法,如中值滤波器。结果表明,在CA的应用中有几个有趣的不变性,如特定的噪声实现和选择像素的子采样来确定重组权值。此外,寻求局部最小值的更简单的权重优化算法似乎与寻求全局最小值(如模拟退火)的算法一样有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信