An Efficient Cumulative Edge-Detection Method for Image Reconstruction

TL;DR

This paper presents CR-ℓ_q-RMM-GKS, a computationally efficient method that combines cumulative edge detection with iterative reweighting for improved image reconstruction, especially in preserving sharp edges.

Contribution

It introduces a novel combination of cumulative edge detection with RMM-GKS, enabling efficient, high-quality image reconstructions with superior edge preservation.

Findings

CR-ℓ_q-RMM-GKS produces sharper edges than non-cumulative methods.

Cumulative weighting and ℓ_1 penalties are highly complementary.

The method outperforms standard ℓ_1 and CR-ℓ_2 approaches in experiments.

Abstract

When reconstructing images from noisy measurements, such as in medical scans or scientific imaging, we face an inverse problem: recovering an unknown image from indirect, corrupted observations. These problems are typically ill-posed, meaning small amounts of noise can lead to inaccurate reconstructions. Regularization techniques address this by incorporating prior assumptions about the solution, such as smoothness or sparsity. However, standard methods often blur sharp edges--the boundaries between tissues or structures--losing critical detail. A powerful strategy for edge preservation is iterative reweighting, which solves a sequence of weighted subproblems with adaptively updated weights. Non-cumulative schemes derive weights from the current iterate alone and can be solved efficiently using the Recycled Majorization-Minimization Generalized Krylov Subspace method (RMM-GKS). The cumulative approach of Gazzola et al. progressively accumulates edge information across iterations, achieving superior edge preservation but at high computational cost. This work introduces CR-$\ell_q$-RMM-GKS, which combines cumulative edge detection with computational efficiency. We integrate Gazzola's cumulative weighting with RMM-GKS, which handles general $\ell_q$ penalties ($0 < q \le 2$), automatically selects regularization parameters, and recycles Krylov subspaces between iterations, reducing the nested structure to two levels. Numerical experiments in signal deblurring and tomography demonstrate that CR-$\ell_q$-RMM-GKS produces significantly sharper edge reconstructions than standard non-cumulative methods. In particular, CR-$\ell_1$-RMM-GKS outperforms both standard $\ell_1$ methods and CR-$\ell_2$-RMM-GKS, indicating that cumulative weighting and $\ell_1$ penalties are highly complementary.