期刊文献+

Research on Destriping Based on Rough Set 被引量:1

Research on Destriping Based on Rough Set
下载PDF
导出
摘要 In order to remove the stripe noises in cotton foreign fiber images by line scanning camera collected, in multi threshold segmentation of rough set, every region’s color is instead of the statistics color of the region. This method can retain the detail information of original image as far as possible, and do well in the stripe noise removal. The roughness of rough set was calculated respectively using directional diagram, Canny operator and Sobel operator. Comparing the three methods, the results indicate that the Canny operator keeps the more details of image, and directional diagram and Sobel operator have the better effects on denoising. In order to remove the stripe noises in cotton foreign fiber images by line scanning camera collected, in multi threshold segmentation of rough set, every region’s color is instead of the statistics color of the region. This method can retain the detail information of original image as far as possible, and do well in the stripe noise removal. The roughness of rough set was calculated respectively using directional diagram, Canny operator and Sobel operator. Comparing the three methods, the results indicate that the Canny operator keeps the more details of image, and directional diagram and Sobel operator have the better effects on denoising.
出处 《Journal of Computer and Communications》 2015年第11期8-12,共5页 电脑和通信(英文)
关键词 STRIPE NOISES Multi THRESHOLD Segmentation ROUGH Set Denosing Stripe Noises Multi Threshold Segmentation Rough Set Denosing
  • 相关文献

参考文献3

二级参考文献35

  • 1金良海,李德华.基于CIELAB空间的开关型矢量中值滤波器[J].小型微型计算机系统,2007,28(9):1700-1704. 被引量:3
  • 2Milton A F, Barone F R, Kruer M R. Influence of nonuni- formity on infrared focal plane array performance[ J]. Op- tical Engineering, 1985,24 ( 5 ) : 855 - 862.
  • 3Friedenberg A, Goldbatt I. Nonuniformity two-point linear correction errors in infrared focal plane arrays [ J ]. Optical Engineering, 1998,37 (4) : 1251 - 1253.
  • 4Harris J G, Chiang Y. Nonuniformity correction of infrared image sequences using the constant statistics constraint [J] IEEE Trans. Image Process, 1999, 8 ( 8 ) : 1148 - 1151.
  • 5Scribner D A, Sarkady K A, Kruer M R, et al. Adaptive retina-like preprocessing for imaging detector arrays [J]// Proc. of IEEE Int. Conf. on Neural Networks, 1993,3 : 1955 - 1960. Harris J G, Chiang Y. Minimizing the ghosting artifact in scene-based nonuniformity correction [ C 1// Proc of SHE, 1998,3377 : 106 - 113.
  • 6Rossi A, Diani M, Corsini G. Temporal statistics de-ghos- ting for adaptive non-uniformity correction in infrared fo- cal plane arrays[ J ]. IET Electron. Lett. , 2010,46 (5): 348 - 349.
  • 7Vera E, Tortes S. Ghosting reduction in adaptive nonuni- formity correction of infrared focal-plane array image se- quences[C]// Proe. of IEEE Int. Conf. Image Process, 2003,3 :II - 1001 -4.
  • 8Hardie RC,BaxleyF,Brys B,et al. Scene-based nonuniformi- ty correction with reduced ghosting using a gated LMS algo- rithm[ J ]. Opt. Express ,2009,17 (17) : 14918 - 14933.
  • 9Delon J. Midway image equalization [ J ]. Journal of Mathe- matical Imaging and Vision ,20tM ,21 (2) : 119 - 134.
  • 10Gilboa G, Sochen N, Zeevi Y Y. Texture preserving varia- tional denoising using an adaptive fidelity term [ C ]// Proc. of VLSM ,2003:137 - 144.

共引文献17

同被引文献22

引证文献1

二级引证文献8

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部