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arXiv · 2601.04999

Guided Variational Network for Image Decomposition

Alessandro Lanza·Serena Morigi·Youwei Wen·Li Yang
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Abstract

Cartoon-texture image decomposition is a critical preprocessing problem bottlenecked by the numerical intractability of classical variational or optimization models and the tedious manual tuning of global regularization parameters.We propose a Guided Variational Decomposition (GVD) model which introduces spatially adaptive quadratic norms whose pixel-wise weights are learned either through local probabilistic statistics or via a lightweight neural network within a bilevel framework.This leads to a unified, interpretable, and computationally efficient model that bridges classical variational ideas with modern adaptive and data-driven methodologies. Numerical experiments on this framework, which inherently includes automatic parameter selection, delivers GVD as a robust, self-tuning, and superior solution for reliable image decomposition.

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Alessandro Lanza, Serena Morigi, Youwei Wen, Li Yang. 2026-01-08. Guided Variational Network for Image Decomposition. https://arxiv.org/abs/2601.04999

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