Adaptive Regularization of Some Inverse Problems in Image Analysis
- Authors
- Hong, Byung-Woo; Koo J.; Burger M.; Soatto S.
- Issue Date
- 2020
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Adaptive Regularization; ADMM; Convex Optimization; Denoising; Huber-Huber Model; Optical Flow; Segmentation
- Citation
- IEEE Transactions on Image Processing, v.29, pp 2507 - 2521
- Pages
- 15
- Journal Title
- IEEE Transactions on Image Processing
- Volume
- 29
- Start Page
- 2507
- End Page
- 2521
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/39029
- DOI
- 10.1109/TIP.2019.2960587
- ISSN
- 1057-7149
1941-0042
- Abstract
- 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. IEEE
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Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
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