基于合成高频带频率和自适应hampel随机函数的快速SISR技术

K. Thakulsukanant, V. Patanavijit
{"title":"基于合成高频带频率和自适应hampel随机函数的快速SISR技术","authors":"K. Thakulsukanant, V. Patanavijit","doi":"10.1109/ICIIBMS.2017.8279729","DOIUrl":null,"url":null,"abstract":"Theoretically, the conventional image enlarging technique is a mathematical method for building a superior enriched resolution image, which is normally insisted for modern computer vision and image processing application by utilizing only one poor resolution image, which is normally captured from any commercial embedded camera systems. Due to the fast computation, the Single-Image Super-Resolution (SISR) is one of the well-known Super Resolution-Reconstruction (SRR) techniques and the SISR is desired for applying on only one poor resolution image. Therefore, this article aims to present the image enlarged technique founded on SISR algorithm utilizing Hampel stochastic function and high-band frequency synthesizing. Unfortunately, the efficacy of the SISR technique is relied upon three parameters (b, h, k) and it is difficult task for estimating these suitable values of these three parameters for reconstructing the superior enriched resolution image with the optimum Peak Signal-to-Noise Ratio (PSNR). In consideration of deciphering to this problem, the Hampel stochastic function, which is relied upon wholly one parameter (J), instead of three parameters like the conventional function, is comprised into SISR technique. By studying on 14 classic images, which are corrupted by different noise models, in the statically exploratory section, the efficacy of the fast SISR technique closely equal to the conventional SISR technique but the parameter adjustment process of the proposed fast SISR technique (with one parameter) is more simple and fasert than the conventional SISR technique (with three parameters). Because of fast computation in the parameter adjustment process, the proposed fast SISR technique is more suitable for real implementation.","PeriodicalId":122969,"journal":{"name":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"364 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A fast SISR technique founded on synthesize high-band frequency and an adaptive hampel stochastic function\",\"authors\":\"K. Thakulsukanant, V. Patanavijit\",\"doi\":\"10.1109/ICIIBMS.2017.8279729\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Theoretically, the conventional image enlarging technique is a mathematical method for building a superior enriched resolution image, which is normally insisted for modern computer vision and image processing application by utilizing only one poor resolution image, which is normally captured from any commercial embedded camera systems. Due to the fast computation, the Single-Image Super-Resolution (SISR) is one of the well-known Super Resolution-Reconstruction (SRR) techniques and the SISR is desired for applying on only one poor resolution image. Therefore, this article aims to present the image enlarged technique founded on SISR algorithm utilizing Hampel stochastic function and high-band frequency synthesizing. Unfortunately, the efficacy of the SISR technique is relied upon three parameters (b, h, k) and it is difficult task for estimating these suitable values of these three parameters for reconstructing the superior enriched resolution image with the optimum Peak Signal-to-Noise Ratio (PSNR). In consideration of deciphering to this problem, the Hampel stochastic function, which is relied upon wholly one parameter (J), instead of three parameters like the conventional function, is comprised into SISR technique. By studying on 14 classic images, which are corrupted by different noise models, in the statically exploratory section, the efficacy of the fast SISR technique closely equal to the conventional SISR technique but the parameter adjustment process of the proposed fast SISR technique (with one parameter) is more simple and fasert than the conventional SISR technique (with three parameters). Because of fast computation in the parameter adjustment process, the proposed fast SISR technique is more suitable for real implementation.\",\"PeriodicalId\":122969,\"journal\":{\"name\":\"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)\",\"volume\":\"364 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIIBMS.2017.8279729\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIIBMS.2017.8279729","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

从理论上讲,传统的图像放大技术是一种建立高分辨率图像的数学方法,这通常是现代计算机视觉和图像处理应用所坚持的,它只利用一幅低分辨率图像,通常从任何商业嵌入式相机系统中捕获。由于计算速度快,单图像超分辨率(SISR)是著名的超分辨率重建(SRR)技术之一,并且SISR仅适用于单幅低分辨率图像。因此,本文旨在利用Hampel随机函数和高频合成技术,提出基于SISR算法的图像放大技术。遗憾的是,SISR技术的有效性依赖于三个参数(b, h, k),并且很难估计这三个参数的合适值以重建具有最佳峰值信噪比(PSNR)的高富集分辨率图像。考虑到对该问题的解密,在SISR技术中加入了完全依赖于一个参数(J)的Hampel随机函数,而不是像常规函数那样依赖于三个参数。通过对静态探索区14幅被不同噪声模型破坏的经典图像的研究,快速SISR技术的效果与常规SISR技术接近,但所提出的快速SISR技术(单参数)的参数调整过程比常规SISR技术(三参数)更简单、快速。由于在参数调整过程中计算速度较快,所提出的快速SISR技术更适合于实际实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A fast SISR technique founded on synthesize high-band frequency and an adaptive hampel stochastic function
Theoretically, the conventional image enlarging technique is a mathematical method for building a superior enriched resolution image, which is normally insisted for modern computer vision and image processing application by utilizing only one poor resolution image, which is normally captured from any commercial embedded camera systems. Due to the fast computation, the Single-Image Super-Resolution (SISR) is one of the well-known Super Resolution-Reconstruction (SRR) techniques and the SISR is desired for applying on only one poor resolution image. Therefore, this article aims to present the image enlarged technique founded on SISR algorithm utilizing Hampel stochastic function and high-band frequency synthesizing. Unfortunately, the efficacy of the SISR technique is relied upon three parameters (b, h, k) and it is difficult task for estimating these suitable values of these three parameters for reconstructing the superior enriched resolution image with the optimum Peak Signal-to-Noise Ratio (PSNR). In consideration of deciphering to this problem, the Hampel stochastic function, which is relied upon wholly one parameter (J), instead of three parameters like the conventional function, is comprised into SISR technique. By studying on 14 classic images, which are corrupted by different noise models, in the statically exploratory section, the efficacy of the fast SISR technique closely equal to the conventional SISR technique but the parameter adjustment process of the proposed fast SISR technique (with one parameter) is more simple and fasert than the conventional SISR technique (with three parameters). Because of fast computation in the parameter adjustment process, the proposed fast SISR technique is more suitable for real implementation.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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