Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems

TL;DR

This paper introduces a novel IRLS-based optimization method with convergence guarantees for inverse imaging problems, enabling learned regularizers that improve image reconstruction tasks like deblurring and super-resolution.

Contribution

It extends IRLS to analysis-based regularization with convergence guarantees and proposes a bilevel learning framework for parameter optimization in image reconstruction.

Findings

Converges linearly to a stationary point under mild conditions.

Outperforms existing unrolled networks in image reconstruction tasks.

Efficiently learns regularizer parameters from training data.

Abstract

In this work we present a novel optimization strategy for image reconstruction tasks under analysis-based image regularization, which promotes sparse and/or low-rank solutions in some learned transform domain. We parameterize such regularizers using potential functions that correspond to weighted extensions of the $\ell_p^p$-vector and $\mathcal{S}_p^p$ Schatten-matrix quasi-norms with $0 < p \le 1$. Our proposed minimization strategy extends the Iteratively Reweighted Least Squares (IRLS) method, typically used for synthesis-based $\ell_p$ and $\mathcal{S}_p$ norm and analysis-based $\ell_1$ and nuclear norm regularization. We prove that under mild conditions our minimization algorithm converges linearly to a stationary point, and we provide an upper bound for its convergence rate. Further, to select the parameters of the regularizers that deliver the best results for the problem at hand, we propose to learn them from training data by formulating the supervised learning process as a stochastic bilevel optimization problem. We show that thanks to the convergence guarantees of our proposed minimization strategy, such optimization can be successfully performed with a memory-efficient implicit back-propagation scheme. We implement our learned IRLS variants as recurrent networks and assess their performance on the challenging image reconstruction tasks of non-blind deblurring, super-resolution and demosaicking. The comparisons against other existing learned reconstruction approaches demonstrate that our overall method is very competitive and in many cases outperforms existing unrolled networks, whose number of parameters is orders of magnitude higher than in our case.