Unrolling Plug-and-Play Gradient Graph Laplacian Regularizer for Image Restoration
Jianghe Cai, Gene Cheung, Fei Chen

TL;DR
This paper introduces interpretable, unrolled graph-based optimization networks for image restoration, combining mathematical rigor with competitive performance and robustness to data shifts.
Contribution
It formulates a new graph Laplacian regularizer for image reconstruction and unrolls ADMM algorithms into trainable neural networks with adaptive graph learning modules.
Findings
Competitive restoration quality with fewer parameters
Enhanced robustness to covariate shift
Effective integration of graph learning in unrolled networks
Abstract
Generic deep learning (DL) networks for image restoration like denoising and interpolation lack mathematical interpretability, require voluminous training data to tune a large parameter set, and are fragile in the face of covariate shift. To address these shortcomings, we build interpretable networks by unrolling variants of a graph-based optimization algorithm of different complexities. Specifically, for a general linear image formation model, we first formulate a convex quadratic programming (QP) problem with a new -norm graph smoothness prior called gradient graph Laplacian regularizer (GGLR) that promotes piecewise planar (PWP) signal reconstruction. To solve the posed unconstrained QP problem, instead of computing a linear system solution straightforwardly, we introduce a variable number of auxiliary variables and correspondingly design a family of ADMM algorithms. We then…
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Taxonomy
TopicsMedical Image Segmentation Techniques · Advanced Image Fusion Techniques · Medical Imaging Techniques and Applications
MethodsAlternating Direction Method of Multipliers
