期刊文献+

基于图像分解和稀疏表示的图像去噪修复方法研究 被引量:1

下载PDF
导出
摘要 传统的图像去噪方法大致可分为空域去噪和变换域去噪两类。常见的图像空域去噪方法包括邻域平均、空域低通滤波、空域中值滤波等。邻域平均法是一种典型的局部空域处理的去噪算法,其缺点是处理后的图像存在一定的模糊度。空域低通滤波方法通过低通卷积模板在图像空域进行二维卷积来达到去除图像噪声的目的。
出处 《计算机光盘软件与应用》 2013年第24期114-115,共2页 Computer CD Software and Application
  • 相关文献

参考文献2

二级参考文献12

  • 1D Donoho. Compressed sensing [J]. IEEE Trans Inform Theory, 2006, 51(4): 1289-1306.
  • 2E Candes, J Romberg, T Tao. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information [J]. IEEE Trans Inform, 2006, 52(2): 489-509.
  • 3M Rudelson, R Vershynin. Sparse reconstruction by convex relaxation: Fourier and Gaussian measurements [A]. In Proc. CISS 2006 (40th Annual Conference on Information Sciences and Systems) [C]. 2006.
  • 4S J Kim, K Koh, M Lustig, S Boyd, D Gorinevsky. A interiorpoint method for large-scale Ll-regularized least-squares problems with applications in signal processing and statistics [J]. Journal of Machine Learning Research, 2007, 7(8): 1519-1555.
  • 5J Tropp, A Gilbert. Signal recovery from random measurements via orthogonal matching pursuit [J]. IEEE Tram. Inform. Theory, 2007, 53(12): 4655-4666.
  • 6Rauhut H, Schnass K, Vandergheynst P. Compressed sensing and redundant dictionaries [J]. IEEE Transactions on Information Theory, 2008, 54(5): 2210-2219.
  • 7S Chen, D Donoho, M Saunders. Atomic decomposition by basis pursuit [J]. SIAMJ Sci Comput, 1999, 20: 33-61.
  • 8Baraniuk R G. Compressive sensing [J]. IEEE Signal Processing Magazine, 2007, 24(4): 118-121.
  • 9D Needell, R Vershynin. Uniform uncertainty principle and signal recovery via regularized orthogonai matching pursuit [J]. Found. Comput Math, 2009, 9(3): 317-334.
  • 10X Mei, LI Ling, Y Wu, E Blasch, L Bai. Minimum error bounded efficient LI tracker with occlusion detection [A]. in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) [C]. 2011.

共引文献2

同被引文献6

引证文献1

二级引证文献2

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