吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (增刊1): 106-109.

Previous Articles     Next Articles

Intensity preserving technology in image restoration

GUO Xiao-xin1,2, XU Zhi-wen1,2, CHE Xiang-jiu1,2   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2. Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun 130012, China
  • Received:2012-06-25 Published:2013-06-01

Abstract:

A new approach of image restoration for the sequence of fluorescein angiography was proposed.First,the intensity constraint was incorporated into Miller regularization equation to obtain the ability to preserve the high intensity of pixels.Thus,the restored image would be influenced not only by the smoothness constraints,but also by the intensity constraints.Secondly,in the process of the restoration,we use an intensity template obtained from the pre-filtering procedure was used to achieve the intensity constraints.The template represented an image composed by the desired intensity value.The experiments show that the proposed scheme gains a better result in both high intensity preservation and image restoration.

Key words: image restoration, intensity constraint, intensity preservation, fluorescein angiography

CLC Number: 

  • TP391.41

[1] Biemond J,Lagendijk R L.Regularized iterative image restoration in a weighted hilbert space [C]//ICASSP'86.1986:1485-1488.

[2] Lagendijk R L,Biemond J,Boekee D E.Regularized iterative image restoration with ringing reduction [J].IEEE Transactions on Acoustics,Speech and Signal Processing,1987,36(12):1874-1888.

[3] Singh S,Tandon S N,Gupta H M.An iterative restoration technique [J].Signal Processing,1986,11(1):1-11.

[4] Castleman K R.Digital Image Processing [M].Upper Saddle River,New Jersey:Prentice Hall International,Inc,1998:64-65.

[5] Miller K.Least-squares method for ill-posed problems with a prescribed bound [C]//SIAM J Math Anal.1970:52-74.

[1] JIANG Chao, GENG Ze-xun, LIU Li-yong, PAN Ying-feng. Maximum likelihood image restoration combined with image denoising [J]. 吉林大学学报(工学版), 2015, 45(4): 1360-1366.
[2] LI Yi-bing, FU Qiang, ZHANG Jing. Underwater blurred image restoration based on parameter estimation [J]. 吉林大学学报(工学版), 2013, 43(04): 1133-1138.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!