ADMM-based residual whiteness principle for automatic parameter selection in super-resolution problems

TL;DR

This paper introduces an automatic parameter selection method for image super-resolution that uses a residual whiteness measure within an ADMM framework, improving regularization parameter tuning for noisy, blurred images.

Contribution

It develops a novel residual whiteness principle based on frequency domain analysis for automatic regularization parameter selection in super-resolution tasks.

Findings

Effective in various super-resolution scenarios

Outperforms traditional discrepancy principle

Compatible with non-convex regularizers

Abstract

We propose an automatic parameter selection strategy for the problem of image super-resolution for images corrupted by blur and additive white Gaussian noise with unknown standard deviation. The proposed approach exploits the structure of both the down-sampling and the blur operators in the frequency domain and computes the optimal regularisation parameter as the one optimising a suitable residual whiteness measure. Computationally, the proposed strategy relies on the fast solution of generalised Tikhonov $\ell_2$-$\ell_2$ problems as proposed in a work from Zhao et al. These problems naturally appear as substeps of the Alternating Direction Method of Multipliers (ADMM) optimisation approach used to solve super-resolution problems with non-quadratic and often non-smooth, sparsity-promoting regularisers both in convex and in non-convex regimes. After detailing the theoretical properties defined in the frequency domain which allow to express the whiteness functional in a compact way, we report an exhaustive list of numerical experiments proving the effectiveness of the proposed approach for different type of problems, in comparison with well-known parameter selection strategy such as, e.g., the discrepancy principle.