State-of-the-art image reconstruction often relies on complex, highly parameterized deep architectures. We propose an alternative: a data-driven reconstruction method inspired by the classic Tikhonov regularization. Our approach iteratively refines intermediate reconstructions by solving a sequence of quadratic problems. These updates have two key components: (i) learned filters to extract salient image features, and (ii) an attention mechanism that locally adjusts the penalty of filter responses. Our method achieves performance on par with leading plug-and-play and learned regularizer approaches while offering interpretability, robustness, and convergent behavior. In effect, we bridge traditional regularization and deep learning with a principled reconstruction approach.
@article{pourya2025dealing,
title={DEALing with Image Reconstruction: Deep Attentive Least Squares},
author={Pourya, Mehrsa and Kobler, Erich and Unser, Michael and Neumayer, Sebastian},
journal={arXiv preprint arXiv:2502.04079},
year={2025}
}