首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Multidirectional Gradient Neighbourhood-Weighted Image Sharpness Evaluation Algorithm
【24h】

Multidirectional Gradient Neighbourhood-Weighted Image Sharpness Evaluation Algorithm

机译:Multidirectional Gradient Neighbourhood-Weighted Image Sharpness Evaluation Algorithm

获取原文
获取原文并翻译 | 示例
           

摘要

Aiming at the problem that the image sharpness evaluation algorithm in the photoelectric system has a slow speed in actual processing and is severely disturbed by noise, an improved image sharpness evaluation algorithm is proposed by combining multiscale decomposition tools and multidirectional gradient neighbourhood weighting. This paper applies non-subsampled shearlet transform (NSST) to perform multiscale transformation of the input images, obtaining high-frequency sub-band images and low-frequency sub-band images. In order to enhance the detection of the edge orientation of images, multidirectional gradient processing of the image matrix is added to each sub-band image. In addition, the weight corresponding to the current pixel is obtained by calculating the inverse ratio of the gradient of each direction and the distance of the center pixel. Through calculating the ratio of the gradient neighbourhood weighting operators of high-frequency sub-band images and low-frequency sub-band images, the image sharpness evaluation value can be acquired further. Moreover, the image sequence collected by a certain type of photoelectric system is selected as the image sequence of the noisy real environment for simulation experiments and compared with the current mainstream algorithms. Finally, the experimental draws a conclusion that compared with the mainstream evaluation algorithms, the evaluation results of the proposed method perform better in terms of steepness, sensitivity, and flat area fluctuation, it can better suppress noise and improve accuracy, and its running speed meets the basic requirements of the image sharpness evaluation algorithm.

著录项

  • 来源
  • 作者

    Yan Xingya; Lei Jian; Zhao Zhi;

  • 作者单位

    Xian Univ Posts & Telecommun, Acad Digital Arts, Xian 710121, Shaanxi, Peoples R China|Shaanxi Normal Univ, Inst Educ, Xian 710062, Shaanxi, Peoples R China;

    Xian Univ Posts & Telecommun, Sch Comp Sci & Technol, Xian 710121, Shaanxi, Peoples R China;

    Northwestern Polytech Univ, Sch Elect Informat, Xian 710129, Shaanxi, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 英语
  • 中图分类
  • 关键词

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号