An Adaptive Norm Algorithm for Image Restoration
We propose an adaptive norm strategy designed for the re-storation of images contaminated by blur and noise. Standard Tikhonov regularization can give good results with Gaussian noise and smooth images, but can over-smooth the output. On the other hand, L1-TV (Total Variation) regularization has superior performance with some non-Gaussian noise and controls both the size of jumps and the geometry of the object boundaries in the image but smooth parts of the recovered images can be blocky. According to a coherence map of the image which is obtained by a threshold structure tensor, and can detect smooth regions and edges in the image, we apply L2-norm or L1-norm regularization to different parts of the image. The solution of the resulting minimization problem is obtained by a fast algorithm based on the half-quadratic technique recently proposed in  for L1-TV regularization. Some numerical results show the effectiveness of our adaptive norm image restoration strategy.
KeywordsCoherence Eter Deblurring Oman
Unable to display preview. Download preview PDF.
- 2.Chan, R.H., Liang, H.X.: A fast and efficient half-quadratic algorithm for TV-L1 Image restoration, CHKU research report 370 (submitted, 2010), ftp://ftp.math.cuhk.edu.hk/report/2010-03.ps.Z
- 15.Reichel, L., Sgallari, F., Ye, Q.: Tikhonov regularization based on generalized Krylov subspace methods. Appl. Numer. Math. (2010), doi:10.1016/j.apnum.2010.10.002Google Scholar
- 18.Chen, Q., Montesinos, P., Sun, Q.S., Heng, P.A., Xia, D.S.: Adaptive total variation denoising based on difference curvature. Image and Vision Computing 28(3), 298–306Google Scholar