Bilevel Programming Approach for Image Restoration Problems with Automatically Hyperparameter Selection

TL;DR

This paper introduces a bilevel programming method for automatic hyperparameter selection in image restoration, improving quality and efficiency over manual tuning.

Contribution

It presents a novel bilevel programming framework with convergence guarantees for automated hyperparameter tuning in image restoration.

Findings

Achieves higher restoration quality than existing methods.

Reduces computational time for hyperparameter tuning.

Demonstrates effectiveness on both simulated and real images.

Abstract

In optimization-based image restoration models, the correct selection of hyperparameters is crucial for achieving superior performance. However, current research typically involves manual tuning of these hyperparameters, which is highly time-consuming and often lacks accuracy. In this paper, we concentrate on the automated selection of hyperparameters in the context of image restoration and present a bilevel programming approach that can simultaneously select the optimal hyperparameters and achieve high-quality restoration results. For implementation, we reformulate the bilevel programming problem that incorporates an inequality constraint related to the difference-of-convex functions. Following this, we address a sequence of nonsmooth convex programming problems by employing a feasibility penalty function along with a proximal point term. In this context, the nonsmooth convex programming problem uses the solution of the lower-level problem, which is derived through the alternating direction method of multipliers. Theoretically, we prove that the sequence generated by the algorithm converges to a Karush-Kuhn-Tucker stationary point of the inequality-constrained equivalent bilevel programming problem. We conduct a series of tests on both simulations and real images, which demonstrate that the proposed algorithm achieve superior restoration quality while requiring less computing time compared to other hyperparameter selection methods.