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


Book Volume: 29

Pages Range: 2507-2521

Article Number: 8941322

DOI: 10.1109/TIP.2019.2960587


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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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.


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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