A Semi-Convergent Stage-Wise Framework with Provable Global Convergence for Adaptive Total Variation Regularization

TL;DR

This paper introduces a semi-convergent, stage-wise framework combining first- and higher-order TV regularizers for image restoration, with provable global convergence and adaptive selection of optimal iterates, improving performance over existing methods.

Contribution

It proposes a novel semi-convergent, stage-wise approach integrating multiple TV regularizers with theoretical guarantees and adaptive iteration selection for enhanced image restoration.

Findings

Achieves superior denoising and deblurring results

Demonstrates monotonic decrease in objective values

Maintains theoretical interpretability and simplicity

Abstract

Image restoration requires a careful balance between noise suppression and structure preservation. While first-order total variation (TV) regularization effectively preserves edges, it often introduces staircase artifacts, whereas higher-order TV removes such artifacts but oversmooths fine details. To reconcile these competing effects, we propose a semi-convergent stage-wise framework that sequentially integrates first- and higher-order TV regularizers within an iterative restoration process implemented via ADMM. Each stage exhibits semi-convergence behavior, i.e., the iterates initially approach the ground truth before being degraded by over-regularization. By monitoring this evolution, the algorithm adaptively selects the locally optimal iterate (e.g., with the highest PSNR) and propagates it as the initial point for the next stage. This select-and-propagate mechanism effectively transfers local semi-convergence into a globally convergent iterative process. We establish theoretical guarantees showing that the sequence of stage-wise iterates is bounded, the objective values decrease monotonically. Extensive numerical experiments on denoising and deblurring benchmarks confirm that the proposed method achieves superior quantitative and perceptual performance compared with conventional first-, higher-order, hybrid TV methods, and learning based methods, while maintaining theoretical interpretability and algorithmic simplicity.