LCS: A learnlet-based sparse framework for blind source separation

¹ Institutes of Computer Science and Astrophysics, Foundation for Research and Technology Hellas (FORTH), Greece
² Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM, 91191 Gif-sur-Yvette, France

^★ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 3 July 2025
Accepted: 22 November 2025

Abstract

Blind source separation (BSS) plays a pivotal role in modern astrophysics by enabling the extraction of scientifically meaningful signals from multi-frequency observations. Traditional BSS methods, such as those that rely on fixed wavelet dictionaries, enforce sparsity during component separation but can fall short when faced with the inherent complexity of real astrophysical signals. In this work, we introduce the learnlet component separator (LCS), a novel BSS framework that bridges classical sparsity-based techniques and modern deep learning. LCS utilises the learnlet transform – a structured convolutional neural network designed to serve as a learned, wavelet-like multi-scale representation. This hybrid design preserves the interpretability and sparsity-promoting properties of wavelets while gaining the adaptability and expressiveness of learned models. The LCS algorithm integrates this learned sparse representation into an iterative source separation process, enabling the effective decomposition of multi-channel observations. While conceptually inspired by sparse BSS methods, LCS introduces a learned representation layer that significantly departs from classical fixed-basis assumptions. We evaluated LCS on both synthetic and real datasets and in this paper demonstrate its superior separation performance compared to state-of-the-art methods (average gain of about 5 dB on toy model examples). Our results highlight the potential of hybrid approaches that combine signal processing priors with deep learning to address the challenges of next-generation cosmological experiments.