期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 29, 期 -, 页码 2507-2521出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2019.2960587
关键词
Adaptive regularization; Huber-Huber model; convex optimization; ADMM; segmentation; optical flow; denoising
资金
- National Research Foundation of Korea (NRF) [NRF-2017R1A2B4006023, NRF-2018R1A4A1059731]
- Office of Naval Research (ONR) [ONR N00014-17-1-2072]
- Army Research Office (ARO) [ARO W911NF17-1-0304]
We present an adaptive regularization scheme for optimizing composite energy functionals arising in image analysis problems. The scheme automatically trades off data fidelity and regularization depending on the current data fit during the iterative optimization, so that regularization is strongest initially, and wanes as data fidelity improves, with the weight of the regularizer being minimized at convergence. We also introduce a Huber loss function in both data fidelity and regularization terms, and present an efficient convex optimization algorithm based on the alternating direction method of multipliers (ADMM) using the equivalent relation between the Huber function and the proximal operator of the one-norm. We illustrate and validate our adaptive Huber-Huber model on synthetic and real images in segmentation, motion estimation, and denoising problems.
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