UPRE method for total variation parameter selection
- Los Alamos National Laboratory
Total Variation (TV) Regularization is an important method for solving a wide variety of inverse problems in image processing. In order to optimize the reconstructed image, it is important to choose the optimal regularization parameter. The Unbiased Predictive Risk Estimator (UPRE) has been shown to give a very good estimate of this parameter for Tikhonov Regularization. In this paper we propose an approach to extend UPRE method to the TV problem. However, applying the extended UPRE is impractical in the case of inverse problems such as de blurring, due to the large scale of the associated linear problem. We also propose an approach to reducing the large scale problem to a small problem, significantly reducing computational requirements while providing a good approximation to the original problem.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 957756
- Report Number(s):
- LA-UR-08-04485; LA-UR-08-4485; TRN: US201016%%161
- Journal Information:
- IEEE Signal Processing Letter, Journal Name: IEEE Signal Processing Letter
- Country of Publication:
- United States
- Language:
- English
Similar Records
A promising limited angular computed tomography reconstruction via segmentation based regional enhancement and total variation minimization
Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data