TY - JOUR
T1 - Selection of regularization parameter in total variation image restoration
AU - Liao, Haiyong
AU - Li, Fang
AU - Ng, Michael K.
PY - 2009/11/1
Y1 - 2009/11/1
N2 - We consider and study total variation (TV) image restoration. In the literature there are several regularization parameter selection methods for Tikhonov regularization problems (e.g., the discrepancy principle and the generalized cross-validation method). However, to our knowledge, these selection methods have not been applied to TV regularization problems. The main aim of this paper is to develop a fast TV image restoration method with an automatic selection of the regularization parameter scheme to restore blurred and noisy images. The method exploits the generalized cross-validation (GCV) technique to determine inexpensively how much regularization to use in each restoration step. By updating the regularization parameter in each iteration, the restored image can be obtained. Our experimental results for testing different kinds of noise show that the visual quality and SNRs of images restored by the proposed method is promising. We also demonstrate that the method is efficient, as it can restore images of size 256 X 256 in = 20 s in the MATLAB computing environment.
AB - We consider and study total variation (TV) image restoration. In the literature there are several regularization parameter selection methods for Tikhonov regularization problems (e.g., the discrepancy principle and the generalized cross-validation method). However, to our knowledge, these selection methods have not been applied to TV regularization problems. The main aim of this paper is to develop a fast TV image restoration method with an automatic selection of the regularization parameter scheme to restore blurred and noisy images. The method exploits the generalized cross-validation (GCV) technique to determine inexpensively how much regularization to use in each restoration step. By updating the regularization parameter in each iteration, the restored image can be obtained. Our experimental results for testing different kinds of noise show that the visual quality and SNRs of images restored by the proposed method is promising. We also demonstrate that the method is efficient, as it can restore images of size 256 X 256 in = 20 s in the MATLAB computing environment.
UR - http://www.scopus.com/inward/record.url?scp=70449721128&partnerID=8YFLogxK
U2 - 10.1364/JOSAA.26.002311
DO - 10.1364/JOSAA.26.002311
M3 - Journal article
C2 - 19884926
AN - SCOPUS:70449721128
SN - 1084-7529
VL - 26
SP - 2311
EP - 2320
JO - Journal of the Optical Society of America A: Optics and Image Science, and Vision
JF - Journal of the Optical Society of America A: Optics and Image Science, and Vision
IS - 11
ER -