Adaptive Regularization of Some Inverse Problems in Image Analysis

Hong BW, Koo J, Burger M, Soatto S (2020)

Publication Type: Journal article

Publication year: 2020

Journal

IEEE Transactions on Image Processing Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 29

Pages Range: 2507-2521

Article Number: 8941322

DOI: 10.1109/TIP.2019.2960587

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.

Authors with CRIS profile

Martin Burger Lehrstuhl für Angewandte Mathematik (Modellierung und Numerik)

Involved external institutions

Chung-Ang University (CAU) KR Korea, Republic of (KR) Technical University of Denmark / Danmarks Tekniske Universitet (DTU) DK Denmark (DK) University of California Los Angeles (UCLA) US United States (USA) (US)

How to cite

APA:

Hong, B.W., Koo, J., Burger, M., & Soatto, S. (2020). Adaptive Regularization of Some Inverse Problems in Image Analysis. IEEE Transactions on Image Processing, 29, 2507-2521. https://dx.doi.org/10.1109/TIP.2019.2960587

MLA:

Hong, Byung Woo, et al. "Adaptive Regularization of Some Inverse Problems in Image Analysis." IEEE Transactions on Image Processing 29 (2020): 2507-2521.

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