CT image denoising based on sparse representation using global dictionary
Low-dose CT (LDCT) images tend to be severely degraded by mottle and streak-like noise,and how to enhance image quality under low-dose CT scanning has attracted more and more attention.This work aims to improve LDCT abdomen image quality through a dictionary learning based de-noising method and accelerate the training time at the same time.The proposed method suppresses noise through reconstructing the image use only one dictionary.Experimental results show that the proposed method is effective in suppressing noise while maintaining the diagnostic image details with much more less time.
Low-dose CT(LDCT) abdomen tumor preprocessing learning dictionary one dictionary
Fei Yu Yang Chen Limin Luo
Laboratory of Image Science and Technology, Southeast University, Nanjing, China Laboratory of Image Science and Technology, Southeast University, Nanjing, China; Centre de Recherch Laboratory of Image Science and Technology, Southeast University, Nanjing, China; Centre de Recherch
国际会议
2013 ICME International Conference on Complex Medical Engineering(2013 ICME复合医学工程国际会议)
北京
英文
408-411
2013-05-25(万方平台首次上网日期,不代表论文的发表时间)