Hyperparameter selection for Discrete Mumford-Shah

TL;DR

This paper introduces a parameter-free method for image denoising and contour detection based on a discrete Mumford-Shah functional, using a Stein-like strategy to automatically select hyperparameters without ground truth data.

Contribution

It develops a theoretically grounded hyperparameter selection method for Mumford-Shah based image processing, eliminating the need for manual tuning or ground truth.

Findings

Accurate hyperparameter estimation demonstrated on synthetic images.

Robustness of the method across different noise levels and geometries.

Effective application to real-world images without prior expertise.

Abstract

This work focuses on a parameter-free joint piecewise smooth image denoising and contour detection. Formulated as the minimization of a discrete Mumford-Shah functional and estimated via a theoretically grounded alternating minimization scheme, the bottleneck of such a variational approach lies in the need to fine-tune their hyperparameters, while not having access to ground truth data. To that aim, a Stein-like strategy providing optimal hyperparameters is designed, based on the minimization of an unbiased estimate of the quadratic risk. Efficient and automated minimization of the estimate of the risk crucially relies on an unbiased estimate of the gradient of the risk with respect to hyperparameters. Its practical implementation is performed using a forward differentiation of the alternating scheme minimizing the Mumford-Shah functional, requiring exact differentiation of the proximity operators involved. Intensive numerical experiments are performed on synthetic images with different geometry and noise levels, assessing the accuracy and the robustness of the proposed procedure. The resulting parameter-free piecewise-smooth estimation and contour detection procedure, not requiring prior image processing expertise nor annotated data, can then be applied to real-world images.